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A concise overview of principal support vector machines and its generalization
Communications for Statistical Applications and Methods 2024;31:235-246
Published online March 31, 2024
© 2024 Korean Statistical Society.

Jungmin Shina, Seung Jun Shin1,a

aDepartment of Statistics, Korea University, Korea
Correspondence to: 1 Department of Statistics, Korea University, 145 Anam-Ro, Seongbuk-Gu, Seoul 02841, Korea. E-mail: sjshin@korea.ac.kr
Received January 19, 2024; Revised February 16, 2024; Accepted February 20, 2024.
In high-dimensional data analysis, sufficient dimension reduction (SDR) has been considered as an attractive tool for reducing the dimensionality of predictors while preserving regression information. The principal support vector machine (PSVM) (Li et al., 2011) offers a unified approach for both linear and nonlinear SDR. This article comprehensively explores a variety of SDR methods based on the PSVM, which we call principal machines (PM) for SDR. The PM achieves SDR by solving a sequence of convex optimizations akin to popular supervised learning methods, such as the support vector machine, logistic regression, and quantile regression, to name a few. This makes the PM straightforward to handle and extend in both theoretical and computational aspects, as we will see throughout this article.
Keywords : sufficient dimension reduction, principal support vector machine, principal machine, M-estimation, convex optimization
1. Introduction

Statistics is essentially about how to reduce the dimension of data without loss of parameter information of interest. There are tons of statistical methods for dimension reduction, and they are broadly categorized into two types: Feature selection and feature extraction. Feature selection, often referred to as variable selection tries to identify a subset of variables to achieve the dimension reduction. Since the seminal LASSO (Tibshirani, 1996) was proposed, penalization becomes a canonical approach for feature selection in supervised learning problems. Popular examples of penalized variable selection methods includes but not limited to Fan and Li (2001), Zou and Hastie (2005), Zou (2006), Hastie et al. (2009), and Zhang (2010). Feature extraction, on the other hand, seeks a transformation of variables. The feature extraction is more popular in an unsupervised learning context and principal component analysis (PCA) (Pearson, 1901) is a canonical example.

Sufficient dimension reduction (SDR) is a feature extraction method for supervised learning, which reduces the predictor dimension while preserving information about the response. Unlike the penalized approaches for the feature selection, SDR is often model-free and has gained great interest in many applications. SDR seeks a matrix B = (b1, . . . , bd) ∈ 꽍p×d, or more precisely span{B}, which satisfies the following assumption:


where 漱 denotes statistical independence. span{B} satisfying (1.1) is referred to as dimension reduction subspace (DRS). When dp, SDR projects the predictors to a lower dimensional subspace while preserving information of Y contained in X. In other word, BX captures all the regression information of Y contained in X. SDR under (1.1) is often referred to as the linear SDR since BTX is a linear function of X. Note that DRS is not unique since any linear transformation of B satisfying (1.1) is DRS. Thereby, the intersection of all DRSes is defined as the central subspace, which is the smallest denoted by , and is the primal identifiable target of interest in SDR. The dimension of the central subspace referred to as a structural dimension is another important quantity to be estimated. Since the exists under mild conditions (Yin et al., 2008), we assume B in (1.1) spans .

There are numerous ways to estimate B, the basis matrix of , and the earliest proposal is the sliced inverse regression (SIR) (Li, 1991). The idea of SIR is simple and based on the following result


and Li (1991) proposed to estimate the inverse regression function E(X|Y) by slicing Y, which explains its name, SIR. The SIR sparked numerous subsequent “inverse” SDR methods based on the conditional moment of x given Y, such as the slice averaged variance estimation (SAVE) (Cook and Weisberg, 1991), contour regression (Li et al., 2005), directional regression (Li and Wang, 2007), cumulative slicing estimation (Zhu et al., 2010), among many others. Since the inverse moment is easily obtained by slicing, the inverse methods are computationally efficient to implement. However, they require untestable assumptions on X, such as the linearity condition and the constant variance condition.

As an alternative to the inverse SDR methods, the “forward” SDR method has been proposed. The forward SDR method achieves SDR by using the conditional expectation of X given Y. The earliest example of the forward SDR method is the principal Hessian direction (pHd) (Li, 1992). The key idea of pHd is that the Hessian matrix of E(Y|X) belongs to the central mean subspace (Li, 1992), denoted by . That is,


The central mean subspace is the minimal subspace of X spanned by B satisfying E(Y|X) = E(Y|BTX), often regarded as an alternative target of . One advantage of the forward approach is that it does not require additional assumptions, such as the linearity condition that is indispensable to inverse methods. There are several forward SDR methods, including a dimension reduction for a multi-index model (Hristache et al., 2001), outer product of gradient method (OPG) (Xia et al., 2002), minimum average variance estimation (MAVE) (Xia et al., 2002), which is a computationally enhanced variant of the OPG estimator. Also, Xia (2007) proposed the dOPG method that extends the idea of the OPG to estimate by replacing the conditional mean. It requires no strong assumptions on the covariates or the functional relation between regressors and the response variable. Thereafter, Kong and Xia (2014) proposed the adaptive composite quantile approach with OPG method (qOPG) for identifying the directions exhaustively, and Kang and Shin (2022) recently proposed weighted OPG (wOPG) for SDR in a binary classification. We also refer to Chapter 11 of Li (2018) for a concise overview of the forward SDR methods.

Cook (2007) and Lee et al. (2013) generalized the linear SDR (1.1) and introduced the nonlinear SDR which seeks a function φ : 꽍p → 꽍d that satisfies


where φ(·) is assumed to be a unique modulo injective transformation of X in order to guarantee its identifiability. The nonlinear SDR methods under (1.4) have been proposed by directly generalizing the idea of the linear SDR methods to nonlinear function via kernel trick. See for example, kernel SIR (Wu, 2008), and kernel canonical correlation analysis developed by Akaho (2001) and Fukumizu et al. (2007), to name a few.

In the meantime, Li et al. (2011) proposed a novel idea of SDR called principal support vector machine (PSVM) that can solve both linear and nonlinear SDR under a unified framework. The principle idea of the PSVM is simple as follows. First, the response Y is dichotomized and a pseudo response Yc is defined as 1 if Yc and −1 otherwise. Then, we train a support vector machine with respect to (Yc,Z), where Z is a standardized predictor. Li et al. (2011) showed that the separating hyperplane obtained by the SVM contains information about .

The beauty of the PSVM comes from the fact that it provides a connection between SDR and the SVM, a conventional supervised learning method. The PSVM can handle both linear and nonlinear SDR in a unified framework, as the SVM does. Moreover, the use of SVM is not essential for estimating and one can replace the hinge loss with other popular covex loss functions. This leads to a variety of extensions of the PSVM. For example, Artemiou and Dong (2016) proposed the principal Lq-SVM with Lq hinge loss, Shin and Artemiou (2017) proposed the principal logistic loss with binomial likelihood loss, Wang et al. (2018) proposed the principal quantile regression with the check loss, and Artemiou et al. (2021) proposed the principal least square SVM with the squared loss.

The PSVM appears to be an inverse SDR method that slices data based on Y, but it can also be viewed as a forward method because it repeatedly applies the usual SVM algorithm to estimate the optimal hyperplanes. Some variants of the PSVM, such as principal weighted SVM (PWSVM) (Shin et al., 2017), principal quantile regression (Wang et al., 2018) do not require slicing Y, i.e., Yc, but training a series of weighted supervised learning with original response. In that, those methods can be regarded as forward SDR approaches. In this regard, the PSVM provides a connection between the inverse and forward SDR methods.

This article is to provide a concise but comprehensive overview of the SDR method that stems from the PSVM, which we will call the principal machine (PM). The rest of the article is organized as follows. In Section 2, we start with briefly reviewing PSVM which serves as a building block of PM. In Section 3, we introduce the principal machine, a general form of the PSVM that covers all of its variants. In Section 4, some miscellaneous including computation and large sample properties are described, respectively, both of which turn out straightforward since it is a simple convex optimization. The penalized principal machine is introduced in Section 5. Concluding remarks are followed in Section 6.

2. Principal support vector machine

Support vector machine (SVM) (Vapnik, 1999) is a well-known binary classifier that seeks an optimal hyperplane to separate two classes. Borrowing the idea of the SVM, Li et al. (2011) proposed the PSVM for the linear SDR (1.1) that minimizes the following objective function:


where Yc denotes the dichotomized pseudo response that takes 1 if Y > c and −1 otherwise, = var(X) is the covariance matrix of X, and x+ = max(0, x). A positive constant λ is a cost parameter that balances the goodness of fit and the complexity of the model, and its choice is not overly sensitive for estimating . We remark that (2.1) is a population level objective function of the linear SVM for (Yc,Z) where Z = 1/2(XEX) is a standardized predictor.

Li et al. (2011) established Theorem 1 that provides the theoretical foundation of the PSVM.

Theorem 1

Assume the linearity condition that E(X|BX) is a linear function of BX, where B is in (1.1). Let (α*, β*) denote the minimizer of (2.1), thenfor any given c.

For a given set of data (Xi, Yi), i = 1, . . . , n, the PSVM estimates as follows by Theorem 1. Given a grid min Yi < c1< · · · < cH < max Yi, the PSVM solves a sequence of objective function (2.2) for different values of ch:


Let (αh,βh) denotes the minimizer of (2.2), which we call the PSVM solution. Then, , is estimated by span(V), where V = (v1, . . . , vd) is a (p × d) matrix whose jth column, vj is the jth leading eigenvector of Mn in (2.3).


Li et al. (2011) showed that the PSVM performs better than classical SDR methods, such as SIR, SAVE, etc.

Due to its similarity to the SVM, one can readily extend the linear PSVM to its nonlinear counter. Namely, the kernel PSVM minimizes the population objective function:


where ψ being a function in a Hilbert space 꼱 of functions of X, ⟨·, ·⟩ is the inner product in 꼱, and ∑ is the covariance operator. Let (α*, ψ*) be the minimizer of (2.4), ψ* is necessarily a function of the sufficient predictor φ(X) in (1.4). A sample version of (2.4) can be readily obtained by employing the reproducing kernel Hilbert space (RKHS) (Wahba, 1999), just like the SVM.

3. Principal machines: A generalization of the PSVM

The PSVM opens up a new class of SDR approaches by connecting the conventional supervised learning algorithm to the SDR context, and achieves both linear and nonlinear SDR in a unified framework. Theorem 1 provides the theoretical foundation of the PSVM and it turns out that we only require the convexity of the hinge loss to prove Theorem 1. It leads to numerous variations of the PSVM by replacing the hinge loss with other convex loss functions.

There are two types of principal machines (PM) depending on how to generalize the idea of the PSVM. One is response-based principal machine (RPM) and the other is loss-based principal machine (LPM).

3.1. Response-based principal machine (RPM)

Note that SVM computes multiple solutions to estimate by generating multiple pseudo responses Yc for different values of c. Variants of RPM are constructed by simply replacing the hinge loss of the PSVM with other convex loss functions.

Assuming X is centered without loss of generality, RPM minimizes the following objective function:


where L(Y, f ) denotes a convex loss function for a linear model f = α + βX to learn Y. We note that (3.1) yields different minimizers as the (pseudo) response Yc changes while the loss function L is fixed, like the name of RPM. Some examples of RPM are presented below and each corresponding loss functions are summarized in Table 1 and visualized in Figure 1.

The PSVM (Li et al., 2011) is a canonical method of RPM. It adopts the hinge loss function for L(Y, f ) as we discussed earlier in (2.1). By noticing that the solution of SVM may not be unique with respect to α, Artemiou and Dong (2016) proposed principal LqPSVM (q > 1) in both linear and nonlinear manners. The loss function of LqSVM is found in Table 1. The objective function of principal LqSVM is strictly convex for both (α, β), which guarantees the uniqueness of the solution.

The advantages of the logistic regression over SVM are obvious since its loss function is smooth and strictly convex. Not only does it entail simpler asymptotic results under less stringent conditions, but it is also computationally stable due to the convexity of the loss. As such, Shin and Artemiou (2017) proposed principal logistic regression (PLR) by applying a binomial log-likelihood (or logistic) loss function.

Recently, Artemiou et al. (2021) proposed a principal least squares SVM (PLSSVM) that uses a least squares loss function. PLSSVM enjoys better estimation of and it also can be used in case of streamed data for an instant real-time update.

3.2. Loss-based principal machine (LPM)

We have just seen how RPM is formulated along with some examples. When Y is binary, however, the possible choice of pseudo response Yc is one. Thus RPM cannot recover exhaustively if . To tackle this issue, Shin et al. (2017) proposed the principal weighted support vector machine (PWSVM) which gets multiple solutions by changing the shape of the loss function via imposing class weights while keeping the response Y fixed. Again, the hinge loss is not the only choice, and LPM refers to PMs that follow this loss-changing idea.

LPM minimizes the following objective function:


where Lc(y, f ) denotes the loss function with an additional paramter c that controls its shape. One standard way to define Lc with binary Y is to introduce class weight as follows.

Lc(Y,f)=π(Y;c)L(Y,f), 듼 듼 듼c(0,1),

where π(Y; c) denotes class weight which takes c if y = 1 and 1 – c otherwise.

One can extend this idea to a regression problem with a continuous response by employing asymmetric loss functions, such as the check loss function for a quantile regression and the asymmetric squared loss function. Below are some examples of LPM, and their loss functions are summarized in Table 2.

The weighted principal support vector machine (PWSVM) (Shin et al., 2017) is an SDR method for a binary classification, which borrows the idea of weighted SVM. It adopts the hinge loss and imposes the class weights differently by the change of the cutoff value c. Shin et al. (2017) showed a fisher consistency of the PWSVM ensures that an estimated hyperplane optimally separates the two classes. Also, by applying weighted logistic loss and weighted least square loss, Kim and Shin (2019) and Jang et al. (2023) suggested the principal weighted logistic regression (PWLR) and principal weighted least square SVM (PWLSSVM), respectively. Those methods effectively estimate for a binary classification.

For a continuous response Y ∈ 꽍, LPM is also demonstrated by applying an asymmetric loss function. Quantile regression (Koenker and Bassett, 1978) is a well-known method for estimating different quantile functions by changing the angle of the check loss over the given quantile τ. As such, Wang et al. (2018) proposed principal quantile regression (PQR) for SDR. PQR chooses the check loss, ρc(u) = u{cI(u < 0)} for each cutoff value, c ∈ (0, 1). PQR echoes the advantages of quantile regression in that it efficiently tackles heteroscedasticity in data and is robust to outliers. Also, the principal asymmetric L2 regression for SDR is proposed by Soale and Dong (PALS, 2022). PALS adopts the asymmetric expectile loss function ρcA(u)=(1-c)u2I(u<0)+cu2I(u0). It performs well in the presence of heteroscedasticity by synthesizing different expectile levels. Other than the above examples, we can imagine any type of LPM by varying the loss function following the cutoff value. It illustrates the extensibility of the PSVM.

4. Miscellaneous

As for a computational aspect, several functions in R programming, such as ipop in kernlab package and quadprog in quadprog package are often used to solve SVM via quadratic programming. These functions are also useful for solving the PSVM, which is regarded to be a sequence of SVMs.

However, when dealing with high-dimensional datasets, quadratic programming is computationally expensive and often unstable. Considering the convexity of the objective function of the PSVM and its variants, the gradient descent (GD) algorithm (including its stochastic version) proves itself a simple and reliable alternative. When coupled with a convex loss function, the GD offers several advantages, including a guaranteed convergence, improved stability, and computational efficiency (Boyd and Vandenberghe, 2004).

Furthermore, the GD for the PSVM is a flexible solver in that the GD only requires a derivative of the objective function, which can be easily calculated if the loss function is specified in advance. Or the derivative of any arbitrary convex function is easily obtained numerically, as long as it retains differentiability and convexity within its support. This flexibility facilitates the development of a unified algorithm for principal sufficient dimension reduction methods.

The convexity of the objective function of the PSVM motivates a direct application of the standard M-estimation theory (Van der Vaart, 2000) and existing asymptotic results of the SVM (Jiang et al., 2008) can mostly be applied in the realm of the PSVM. Consistency and asymptotic normality of the estimates (α,β) in (2.2) are well-addressed in Section 7 of Li et al. (2011) along with proofs and regularity conditions.

Estimating the dimension d in (1.1) of the central subspace, referred to as structural dimension is also a crucial component of sufficient dimension reduction. In this regard, Li et al. (2011) proposed the BIC-type criterion for estimating d as below:


where v1 ≥ · · · ≥ vp(p > d) are eigenvalues of Mn in (2.3). Following the lines of Theorem 8 of Li et al. (2011) and Theorem 4 of Shin et al. (2017), the BIC-type criterion in (4.1) is proofed as a consistent estimator by showing limnP(d^=d)=1. A tuning parameter ρ can be achieved by a cross validation.

5. Sparse principal machines via penalization

Interpreting the estimates of B in (1.1) is not straightforward. This difficulty arises from the fact that a reduced dimension BX represents a linear combination of all original variables. This obscures the underlying relationship between regressors and response.

To illustrate this issue more clearly, let’s consider a simple model Y=exp(-β0X)+0.1, where X ∈ 꽍6, and all predictors and the ε are independent standard normal variables. Assume that the central subspace is spanned by β0=(1,-1,1,0,0,0)/3. However, SDR methods, such as the PSVM, often estimate a vector β that includes all predictors, even when some may not be truly relevant. For example, even if the PSVM can estimated β = (0.933, −0.912, 0.891, 0.021, 0.009, 0.003) based on finite random samples, the last three coefficients are comparatively tiny, but they still contribute to the estimates, blurring the fact that β0 only contains the first three predictors. This demonstrates the difficulty of interpreting SDR results due to the mixing of information across all dimensions (Li, 2007).

In the meantime, sparse SDR approaches aim to achieve both dimension reduction and variable selection simultaneously by providing a sparse representation of the basis for the central subspace . In this regard, several sparse SDR methods have been proposed. See, for example, Li (2007), Bondell and Li (2009), Chun and Kele힊 (2010), and Wu and Li (2011).

Sparse SDR assumes a unique partition X=(X+,X-) that satisfies


where X+ ∈ 꽍q and X ∈ 꽍pq for some q(돦p) (Bondell and Li, 2009; Cook, 2004). We call X+ and X relevant and irrelevant variables, respectively. This partitioning leads to conditional independence. Here, we assume the first q predictors (of a total of p) constitute the relevant subset. This assumption and the conditional independence imply that the last pq rows of the matrix B are zero, which makes B sparse, but also enforces identical sparsity patterns across its columns.

Likewise, a strategy for a variable selection in a regression problem (Among many others, Tibshirani, 1996; Fan and Li, 2001; Zou and Hastie, 2005; Hastie et al., 2009), the sparsity of B can also be achieved by a penalization.

Given a set of data (Xi, Yi), a sample level objective function of sparse SDR based on RPM is given by (5.2).


where assumed Xi is centered, pγ(·) denotes a non-convex penalty function, such as SCAD (Fan and Li, 2001) and group lasso (Yuan and Lin, 2006), which are modulated by a tuning parameter γ that controls the level of the sparsity of the solution. Here Yh and βh=(β1h,β2h,,βph) are the pseudo response and the corresponding coefficient vector with a given cutoff value ch, h = 1, 2, . . . , H, respectively. By penalizing the maximum of |βjh| over h = 1, 2, . . . , H for a given j = 1, 2, . . . , p, the penalty forces the entire elements within the same row of B to shrink towards zero, achieving the desired sparsity structure. The SCAD penalty has gained popularity in variable selection contexts, due to its provable oracle property (Fan and Li, 2001).

For example, Shin and Artemiou (PPLR, 2017) proposed penalized principal logistic regression. This method utilizes the max-SCAD penalty (Fan and Li, 2001) applied to the PLR method described in section 3.1. The oracle property of the PPLR is addressed with detailed explanation and proof.

6. Discussion

Principal support vector machine (PSVM) (Li et al., 2011) offers a novel and powerful solution to a sufficient dimension reduction. We briefly review various aspects of the PSVM, including its linear and nonlinear models, the sample level estimation process, and some theoretical properties. Also, the extensibility of the PSVM is addressed via LPM and RPM frameworks. Sparse SDR applies a penalization method to perform dimension reduction and feature selection simultaneously to improve the interpretability of the SDR result. We also discussed the computational usefulness of the gradient descent algorithm.

This paper investigates the application of the PSVM for high-dimensional data analysis, based on the scenario where the data dimension p can be large but fixed. Researchers will encounter significant analytical challenges as p diverges (p→∞). Estimating the covariance matrix of predictors under the PSVM scheme becomes difficult, necessitating stricter conditions to assure the validity of covariance matrix estimators as p increases (Wu and Li, 2011; Yin and Hilafu, 2015). This issue can be addressed by combining the PSVM with a large covariance matrix estimation method (Bickel and Levina, 2008).

The vast size of datasets inevitably entails a computational burden. One possible solution is to exploit the distributed computing (Forero et al., 2010; Jin et al., 2019). In the case where the data is collected in a stream-wise manner, typically done online, the question will be asked as to how an existing estimator can be effectively updated as new data streams become available. In response to this, Artemiou et al. (2021) proposed the principal least squares support vector machine (PLSSVM), which enables real-time sufficient dimension reduction particularly useful for online learning and streaming data scenarios. This opens further research topics of SDR for large-scale datasets.

Reducing high-dimensional data to lower dimensions through SDR aims to improve the accuracy and efficiency of regression or classification tasks. However, a common pitfall lies in treating the estimated sufficient predictors as the true ones, leading to overestimated confidence levels and biased results. Addressing these troubles, several studies including Kim et al. (2020) have explored post-dimension reduction inference. Despite these efforts, existing methods may lack effectiveness across all situations. Further researches are also needed on this issue.

In addition, we provide several potential areas for exploration and improvement in PSVM. First, PSVM primarily relies on data, but incorporating prior knowledge about the relationship between variables could potentially improve performance as Reich et al. (2011) proposed SDR via Bayesian mixture modeling and Power and Dong (2021) suggested Bayesian model averaging sliced inverse regression. Second, extending PSVM to handle ordinal or mixed data types would broaden its applicability (Bura et al., 2022; Quach and Li, 2023). Lastly, following a trend of broad application of deep neural networks (DNN), the DNN framework can also be applied to the principal sufficient dimension reduction methods. Banijamali et al. (2018) proposed a deep variational approach for sufficient dimension reduction, and Kapla et al. (2022) suggested a fusing SDR with neural networks.

Like many existing dimension reduction approaches, PSVM is a new method that aims to solve challenging high-dimensional problems. So far, it has been difficult to find literature on its application to real-world problems. However, tailoring PSVM to specific domains such as bioinformatics (Li, 2010), text analysis (Kim et al., 2005), or survey analysis (Weng and Young, 2017) could significantly improve its impact on related research areas.


This work is funded by the National Research Foundation of Korea (NRF) grants (2023R1A2C1006587, 2022M3J6A1063595) and Korea University (K2302021).

Fig. 1. SVM(hinge), L2hinge (colored in red), logistic (binomial log-likelihood, colored in blue), and least squares (ls, colored in green) loss functions are visualized. The loss functions are smooth, convex, and continuously differentiable functions across the support, except for the hinge loss (solid black).

Table 1

Different types of the convex loss functions for response-based principal machine along with the corresponding methods


L(Yc, f ) [1 − Yc f ]+ ([1 − Yc f]+])q ln {1 + e(Yc f )} (1 − Yc f)2

Table 2

Different types of the convex loss functions for loss-based principal machine along with the corresponding methods


Lc(Y, f ) π(Y; c) [(1 − Y f)]+ π(Y; c) ln{1 + e(−Y f)} π(Y; c) (1 − Y f)2 πc (Yf) ρcA(Y-f)

*ρc(u) = u{cI(u < 0)}, ρcA(u)=(1-c)u2I(u<0)+cu2I(u0)

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