Multiple Kernel Learning for Adaptive Graph Regularized Nonnegative Matrix Factorization

In this paper a novel face recognition algorithm, based on wavelet kernel non-negative matrix factorization (WKNMF), is proposed. By utilizing features from multi-resolution analysis, the nonlinear mapping capability of kernel nonnegative matrix factorization could be improved by the method proposed. The proposed face recognition method combines wavelet kernel non-negative matrix factorization and RBF network. Extensive experimental results on ORL and YALE face database show that the suggested method possesses much stronger analysis capability than the comparative methods. Compared with PCA, non-negative matrix factorization, kernel PCA and independent component analysis, the proposed face recognition method with WKNMF and RBF achieves over 10 % improvement in recognition accuracy.

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“…From (13), (14) and 15, a wavelet kernel function satisfying the requirements of Mercer kernel function is built as:…”

Section: Wavelet Kernel Non-negative Matrix Factorizationmentioning

confidence: 99%

“…Since the middle of 1990-ies, the kernel method has been successfully applied. Many nonlinear feature extraction methods based on the kernel method have been proposed [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Based on Wavelet Kernel Non-Negative Matrix Factorization

Bai

Hui

2014

Cybernetics and Information Technologies

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“…Furthermore, one requirement of NMF is that the values of data should be non-negative, while in many real world problems the non-negative constraints cannot be satisfied. Since the mid1990s, nuclear method has been successfully applied in the future, there are many scholars have proposed Nonlinear feature extraction method based on kernel method [10][11][12][13].…”

Section: Rvm Classification Of Hyperspectral Images Based On Wavelet mentioning

confidence: 99%

RVM Classification of Hyperspectral Images Based on Wavelet Kernel Non-negative Matrix Fractorization

Bai

Hui

et al. 2015

TELKOMNIKA

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Representing Data by Sparse Combination of Contextual Data Points for Classification

Wang

Zhou

Yin

et al. 2015

Advances in Neural Networks – ISNN 2015

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Abstract. In this paper, we study the problem of using contextual data points of a data point for its classification problem. We propose to represent a data point as the sparse linear reconstruction of its context, and learn the sparse context to gather with a linear classifier in a supervised way to increase its discriminative ability. We proposed a novel formulation for context learning, by modeling the learning of context reconstruction coefficients and classifier in a unified objective. In this objective, the reconstruction error is minimized and the coefficient sparsity is encouraged. Moreover, the hinge loss of the classifier is minimized and the complexity of the classifier is reduced. This objective is optimized by an alternative strategy in an iterative algorithm. Experiments on three benchmark data set show its advantage over state-of-the-art context-based data representation and classification methods.

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Multiple Kernel Learning for Adaptive Graph Regularized Nonnegative Matrix Factorization

Cited by 9 publications

References 0 publications

Face Recognition Based on Wavelet Kernel Non-Negative Matrix Factorization

Face Recognition Based on Wavelet Kernel Non-Negative Matrix Factorization

RVM Classification of Hyperspectral Images Based on Wavelet Kernel Non-negative Matrix Fractorization

Representing Data by Sparse Combination of Contextual Data Points for Classification

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