Fisher discrimination based low rank matrix recovery for face recognition

Zheng, Zhonglong; Yu, Mudan; Jia, Jiong; Liu, Huawen; Xiang, Dao-Hong; Huang, Xiaoqiao; Yang, Jie

doi:10.1016/j.patcog.2014.05.001

Cited by 28 publications

(5 citation statements)

References 39 publications

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“…That is to say, we hope that the selected features should make the distance between the samples from the same class small and distance between the samples from different classes as large as possible. Fisher criterion has been widely used to learn discriminative subspace,, and it is usually defined as to minimize the trace ratio tr ( S W ( X ))/ tr ( S B ( X )), where tr (

\cdot

) means the trace of a matrix. Instead of minimizing the trace ratio, another commonly used variant of the Fisher criterion is to minimize the trace difference which is defined as:

true \min_{W} t r (W^{T} (S_{W} (X) - α S_{B} (X)) W)

…”

Section: Methodsmentioning

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.

show abstract

\cdot

) means the trace of a matrix. Instead of minimizing the trace ratio, another commonly used variant of the Fisher criterion is to minimize the trace difference which is defined as:

true \min_{W} t r (W^{T} (S_{W} (X) - α S_{B} (X)) W)

…”

Section: Methodsmentioning

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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show abstract

“…Their model can also recover the correct column space of data. The linear low-rank recovery has been applied to many computer vision tasks, such as face recognition [14] and image classification [15], where they perform very well. Besides, for low-rank matrix recovery, Liu et al [16] propose a fast tri-factorization method, and Cui et al [17] come up with a transformed affine matrix rank minimization method.…”

Section: Linear Low-rank Recoverymentioning

confidence: 99%

Matrix recovery with implicitly low-rank data

Xie

Liu

et al. 2019

Neurocomputing

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In this paper, we study the problem of matrix recovery, which aims to restore a target matrix of authentic samples from grossly corrupted observations. Most of the existing methods, such as the well-known Robust Principal Component Analysis (RPCA), assume that the target matrix we wish to recover is low-rank. However, the underlying data structure is often non-linear in practice, therefore the low-rankness assumption could be violated. To tackle this issue, we propose a novel method for matrix recovery in this paper, which could well handle the case where the target matrix is low-rank in an implicit feature space but high-rank or even full-rank in its original form. Namely, our method pursues the low-rank structure of the target matrix in an implicit feature space. By making use of the specifics of an accelerated proximal gradient based optimization algorithm, the proposed method could recover the target matrix with non-linear structures from its corrupted version.Comprehensive experiments on both synthetic and real datasets demonstrate the superiority of our method.

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“…Zheng et al [43] addressed a low-rank matrix recovery algorithm with fisher discrimination regularization for face recognition. D 2 L 2 R 2 [6] seeks low-rank dictionary for each class.…”

Section: Low-rank Learningmentioning

confidence: 99%

Multi-view low-rank dictionary learning for image classification

Jing

You

et al. 2016

Pattern Recognition

123

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Fisher discrimination based low rank matrix recovery for face recognition

Cited by 28 publications

References 39 publications

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

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

Matrix recovery with implicitly low-rank data

Multi-view low-rank dictionary learning for image classification

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Fisher discrimination based low rank matrix recovery for face recognition

Cited by 28 publications

References 39 publications

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

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

Matrix recovery with implicitly low-rank data

Multi-view low-rank dictionary learning for image classification

Contact Info

Product

Resources

About

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

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