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Human Detection Using Partial Least Squares Analysis
"... Significant research has been devoted to detecting people in images and videos. In this paper we describe a human detection method that augments widely used edgebased features with texture and color information, providing us with a much richer descriptor set. This augmentation results in an extreme ..."
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Cited by 115 (18 self)
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Significant research has been devoted to detecting people in images and videos. In this paper we describe a human detection method that augments widely used edgebased features with texture and color information, providing us with a much richer descriptor set. This augmentation results in an extremely highdimensional feature space (more than 170,000 dimensions). In such highdimensional spaces, classical machine learning algorithms such as SVMs are nearly intractable with respect to training. Furthermore, the number of training samples is much smaller than the dimensionality of the feature space, by at least an order of magnitude. Finally, the extraction of features from a densely sampled grid structure leads to a high degree of multicollinearity. To circumvent these data characteristics, we employ Partial Least Squares (PLS) analysis, an efficient dimensionality reduction technique, one which preserves significant discriminative information, to project the data onto a much lower dimensional subspace (20 dimensions, reduced from the original 170,000). Our human detection system, employing PLS analysis over the enriched descriptor set, is shown to outperform stateoftheart techniques on three varied datasets including the popular INRIA pedestrian dataset, the lowresolution grayscale DaimlerChrysler pedestrian dataset, and the ETHZ pedestrian dataset consisting of fulllength videos of crowded scenes. 1.
Sparse partial least squares regression for simultaneous dimension reduction and variable selection
 J.R. Statist. Soc.B
"... Summary. Analysis of modern biological data often involves illposed problems due to high dimensionality and multicollinearity. Partial Least Squares (pls) regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since 1960s. ..."
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Cited by 47 (0 self)
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Summary. Analysis of modern biological data often involves illposed problems due to high dimensionality and multicollinearity. Partial Least Squares (pls) regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since 1960s. At the core of the pls methodology lies a dimension reduction technique coupled with a regression model. Although pls regression has been shown to achieve good predictive performance, it is not particularly tailored for variable/feature selection and therefore often produces linear combinations of the original predictors that are hard to interpret due to high dimensionality. In this paper, we investigate the known asymptotic properties of the pls estimator and show that its consistency property no longer holds with the very large p and small n paradigm. We, then, propose a sparse partial least squares (spls) formulation which aims to simultaneously achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of spls regression based on the lars algorithm and benchmark the proposed method by comparisons to well known variable selection and dimension reduction approaches via simulation experiments. An additional advantage of the spls regression is its ability to handle multivariate responses without much additional computational cost. We illustrate this in a joint analysis of gene expression and genomewide binding data. 1.
Bypassing Synthesis: PLS for Face Recognition with Pose, LowResolution and Sketch
"... This paper presents a novel way to perform multimodal face recognition. We use Partial Least Squares (PLS) to linearly map images in different modalities to a common linear subspace in which they are highly correlated. PLS has been previously used effectively for feature selection in face recogniti ..."
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Cited by 40 (4 self)
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This paper presents a novel way to perform multimodal face recognition. We use Partial Least Squares (PLS) to linearly map images in different modalities to a common linear subspace in which they are highly correlated. PLS has been previously used effectively for feature selection in face recognition. We show both theoretically and experimentally that PLS can be used effectively across modalities. We also formulate a generic intermediate subspace comparison framework for multimodal recognition. Surprisingly, we achieve high performance using only pixel intensities as features. We experimentally demonstrate the highest published recognition rates on the pose variations in the PIE data set, and also show that PLS can be used to compare sketches to photos, and to compare images taken at different resolutions. 1.
Partial Least Squares Regression for Graph Mining
"... Attributed graphs are increasingly more common in many application domains such as chemistry, biology and text processing. A central issue in graph mining is how to collect informative subgraph patterns for a given learning task. We propose an iterative mining method based on partial least squares r ..."
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Cited by 32 (5 self)
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Attributed graphs are increasingly more common in many application domains such as chemistry, biology and text processing. A central issue in graph mining is how to collect informative subgraph patterns for a given learning task. We propose an iterative mining method based on partial least squares regression (PLS). To apply PLS to graph data, a sparse version of PLS is developed first and then it is combined with a weighted pattern mining algorithm. The mining algorithm is iteratively called with different weight vectors, creating one latent component per one mining call. Our method, graph PLS, is efficient and easy to implement, because the weight vector is updated with elementary matrix calculations. In experiments, our graph PLS algorithm showed competitive prediction accuracies in many chemical datasets and its efficiency was significantly superior to graph boosting (gBoost) and the naive method based on frequent graph mining.
A Robust and Scalable Approach to Face Identification
"... Abstract. The problem of face identification has received significant attention over the years. For a given probe face, the goal of face identification is to match this unknown face against a gallery of known people. Due to the availability of large amounts of data acquired in a variety of condition ..."
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Cited by 30 (12 self)
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Abstract. The problem of face identification has received significant attention over the years. For a given probe face, the goal of face identification is to match this unknown face against a gallery of known people. Due to the availability of large amounts of data acquired in a variety of conditions, techniques that are both robust to uncontrolled acquisition conditions and scalable to large gallery sizes, which may need to be incrementally built, are challenges. In this work we tackle both problems. Initially, we propose a novel approach to robust face identification based on Partial Least Squares (PLS) to perform multichannel feature weighting. Then, we extend the method to a treebased discriminative structure aiming at reducing the time required to evaluate novel probe samples. The method is evaluated through experiments on FERET and FRGC datasets. In most of the comparisons our method outperforms stateofart face identification techniques. Furthermore, our method presents scalability to large datasets.
Simultaneous Dimensionality Reduction and Human Age Estimation via Kernel Partial Least Squares Regression
"... Human age estimation has recently become an active research topic in computer vision and pattern recognition, because of many potential applications in reality. In this paper we propose to use the kernel partial least squares (KPLS) regression for age estimation. The KPLS (or linear PLS) method has ..."
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Cited by 21 (2 self)
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Human age estimation has recently become an active research topic in computer vision and pattern recognition, because of many potential applications in reality. In this paper we propose to use the kernel partial least squares (KPLS) regression for age estimation. The KPLS (or linear PLS) method has several advantages over previous approaches: (1) the KPLS can reduce feature dimensionality and learn the aging function simultaneously in a single learning framework, instead of performing each task separately using different techniques; (2) the KPLS can find a small number of latent variables, e.g., 20, to project thousands of features into a very lowdimensional subspace, which may have great impact on realtime applications; and (3) the KPLS regression has an output vector that can contain multiple labels, so that several related problems, e.g., age estimation, gender classification, and ethnicity estimation can be solved altogether. This is the first time that the kernel PLS method is introduced and applied to solve a regression problem in computer vision with high accuracy. Experimental results on a very large database show that the KPLS is significantly better than the popular SVM method, and outperform the stateoftheart approaches in human age estimation. 1.
1 Canonical Correlation Analysis for MultiLabel Classification: A Least Squares Formulation, Extensions and Analysis
"... Abstract—Canonical Correlation Analysis (CCA) is a wellknown technique for finding the correlations between two sets of multidimensional variables. It projects both sets of variables onto a lowerdimensional space in which they are maximally correlated. CCA is commonly applied for supervised dimens ..."
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Cited by 19 (1 self)
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Abstract—Canonical Correlation Analysis (CCA) is a wellknown technique for finding the correlations between two sets of multidimensional variables. It projects both sets of variables onto a lowerdimensional space in which they are maximally correlated. CCA is commonly applied for supervised dimensionality reduction in which the two sets of variables are derived from the data and the class labels, respectively. It is wellknown that CCA can be formulated as a least squares problem in the binaryclass case. However, the extension to the more general setting remains unclear. In this paper, we show that under a mild condition which tends to hold for highdimensional data, CCA in the multilabel case can be formulated as a least squares problem. Based on this equivalence relationship, efficient algorithms for solving least squares problems can be applied to scale CCA to very large data sets. In addition, we propose several CCA extensions including the sparse CCA formulation based on the 1norm regularization. We further extend the least squares formulation to partial least squares. In addition, we show that the CCA projection for one set of variables is independent of the regularization on the other set of multidimensional variables, providing new insights on the effect of regularization on CCA. We have conducted experiments using benchmark data sets. Experiments on multilabel data sets confirm the established equivalence relationships. Results also demonstrate the effectiveness and efficiency of the proposed CCA extensions. Index Terms—Canonical correlation analysis, least squares, multilabel learning, partial least squares, regularization 1
Surrogate Variable Analysis Using Partial Least Squares (SVAPLS) in Gene Expression Studies
 2012; :Bioinformatics
"... Motivation: In a typical gene expression profiling study, our prime objective is to identify the genes that are differentially expressed between the samples from two different tissue types. Commonly, standard ANOVA/regression is implemented to identify the relative effects of these genes over the tw ..."
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Cited by 11 (1 self)
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Motivation: In a typical gene expression profiling study, our prime objective is to identify the genes that are differentially expressed between the samples from two different tissue types. Commonly, standard ANOVA/regression is implemented to identify the relative effects of these genes over the two types of samples from their respective arrays of expression levels. But, this technique becomes fundamentally flawed when there are unaccounted sources of variability in these arrays (latent variables attributable to different biological, environmental or other factors relevant in the context). These factors distort the true picture of differential gene expression between the two tissue types and introduce spurious signals of expression heterogeneity. As a result many genes which are actually differentially expressed are not detected, whereas many others are falsely identified as positives. Moreover, these distortions can be
A Least Squares Formulation for a Class of Generalized Eigenvalue Problems in Machine Learning
"... Many machine learning algorithms can be formulated as a generalized eigenvalue problem. One major limitation of such formulation is that the generalized eigenvalue problem is computationally expensive to solve especially for largescale problems. In this paper, we show that under a mild condition, a ..."
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Cited by 10 (1 self)
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Many machine learning algorithms can be formulated as a generalized eigenvalue problem. One major limitation of such formulation is that the generalized eigenvalue problem is computationally expensive to solve especially for largescale problems. In this paper, we show that under a mild condition, a class of generalized eigenvalue problems in machine learning can be formulated as a least squares problem. This class of problems include classical techniques such as