Sparse discriminative feature selection

Yan, Hui; Yang, Jian

doi:10.1016/j.patcog.2014.10.021

Cited by 50 publications

(11 citation statements)

References 21 publications

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“…In the practical IC problems, some normalization steps, such as mean normalization [31] or norm normalization [23] , always conducted in advance. These steps can regulate the distribution of input data and enhance the numerical stability of classifiers, but the consequence has not been analyzed in theory and empirically.…”

Section: The Norm Normalization Problemmentioning

confidence: 99%

Kernel group sparse representation classifier via structural and non-convex constraints

et al. 2018

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In this paper, we propose a new classifier named kernel group sparse representation via structural and non-convex constraints (KGSRSN) for image recognition. The new approach integrates both group sparsity and structure locality in the kernel feature space and then penalties a non-convex function to the representation coefficients. On the one hand, by mapping the training samples into the kernel space, the so-called norm normalization problem will be naturally alleviated. On the other hand, an interval for the parameter of penalty function is provided to promote more sparsity without sacrificing the uniqueness of the solution and robustness of convex optimization. Our method is computationally efficient due to the utilization of the Alternating Direction Method of Multipliers (ADMM) and Majorization-Minimization (MM). Experimental results on three real-world benchmark datasets, i.e., AR face database, PIE face database and MNIST handwritten digits database, demonstrate that KGSRSN can achieve more discriminative sparse coefficients, and it outperforms many state-of-the-art approaches for classification with respect to both recognition rates and running time.

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Section: The Norm Normalization Problemmentioning

confidence: 99%

Kernel group sparse representation classifier via structural and non-convex constraints

et al. 2018

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“…On the other hand, sparse feature selection methods have received increasing attention [29]. By formulating feature selection as a regression model with an ordinary least square (OLS) term and a specifically designed sparsity inducing regularizer, the regression model can be efficiently represented by a linear combination of a set of the most active variables.…”

Section: Introductionmentioning

confidence: 99%

Identifying the most informative features using a structurally interacting elastic net

et al. 2019

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Feature selection can efficiently identify the most informative features with respect to the target feature used in training. However, state-of-the-art vector-based methods are unable to encapsulate the relationships between feature samples into the feature selection process, thus leading to significant information loss. To address this problem, we propose a new graph-based structurally interacting elastic net method for feature selection. Specifically, we commence by constructing feature graphs that can incorporate pairwise relationship between samples. With the feature graphs to hand, we propose a new information theoretic criterion to measure the joint relevance of different pairwise feature combinations with respect to the target feature graph representation. This measure is used to obtain a structural interaction matrix where the elements represent the proposed information theoretic measure between feature pairs. We then formulate a new optimization model through the combination of the structural interaction matrix and an elastic net regression model for the feature subset selection problem. This allows us to a) preserve the information of the original vectorial space, b) remedy the information loss of the original feature space caused by using graph representation, and c) promote a sparse solution and also encourage correlated features to be selected. Because the proposed optimization problem is non-convex, we develop an efficient alternating direction multiplier method (ADMM) to locate the optimal solutions. Extensive experiments on various datasets demonstrate the effectiveness of the proposed method.

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“…Although these methods can rank the features by the weights, they are maybe less effective and efficient because the importance of each feature is evaluated individually and global optimality is neglected. Therefore, sparsity regularization has been introduced into feature selection and received an increasing amount of interest in recent years . Different from traditional feature searching strategies, this type of methods based on sparsity regularization selects the best feature subset in a batch mode so that the interaction and dependency between different features are considered at the same time.…”

Section: Introductionmentioning

confidence: 99%

Joint L_2,1 Norm and Fisher Discrimination Constrained Feature Selection for Rational Synthesis of Microporous Aluminophosphates

Wang

et al. 2016

Molecular Informatics

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Feature selection has been regarded as an effective tool to help researchers understand the generating process of data. For mining the synthesis mechanism of microporous AlPOs, this paper proposes a novel feature selection method by joint l norm and Fisher discrimination constraints (JNFDC). In order to obtain more effective feature subset, the proposed method can be achieved in two steps. The first step is to rank the features according to sparse and discriminative constraints. The second step is to establish predictive model with the ranked features, and select the most significant features in the light of the contribution of improving the predictive accuracy. To the best of our knowledge, JNFDC is the first work which employs the sparse representation theory to explore the synthesis mechanism of six kinds of pore rings. Numerical simulations demonstrate that our proposed method can select significant features affecting the specified structural property and improve the predictive accuracy. Moreover, comparison results show that JNFDC can obtain better predictive performances than some other state-of-the-art feature selection methods.

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Sparse discriminative feature selection

Cited by 50 publications

References 21 publications

Kernel group sparse representation classifier via structural and non-convex constraints

Kernel group sparse representation classifier via structural and non-convex constraints

Identifying the most informative features using a structurally interacting elastic net

Joint L_2,1 Norm and Fisher Discrimination Constrained Feature Selection for Rational Synthesis of Microporous Aluminophosphates

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Sparse discriminative feature selection

Cited by 50 publications

References 21 publications

Kernel group sparse representation classifier via structural and non-convex constraints

Kernel group sparse representation classifier via structural and non-convex constraints

Identifying the most informative features using a structurally interacting elastic net

Joint L2,1 Norm and Fisher Discrimination Constrained Feature Selection for Rational Synthesis of Microporous Aluminophosphates

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Joint L_2,1 Norm and Fisher Discrimination Constrained Feature Selection for Rational Synthesis of Microporous Aluminophosphates