Self-eliminating Discriminant Analysis Dictionary Learning for Pattern Classification

Du, Haishun; Zhang, Yonghao; Li, Zhaoyang; Liu, Panting; Wang, Dingyi

doi:10.1007/s11063-023-11234-z

Cited by 1 publication

(1 citation statement)

References 40 publications

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“…These methods can be broadly categorized into discriminative synthesis DL (SDL) methods, [13][14][15][16][17][18][19][20][21] discriminative analysis DL (ADL) methods, [22][23][24][25][26][27][28] and discriminative analysis-synthesis dictionary pair learning (ASDPL) methods. [29][30][31][32][33][34][35] The discriminative SDL methods, which impose discriminative criteria on synthesis dictionary and sparse codes, learn a discriminant synthesis dictionary by solving a minimizing reconstruction error problem. The learned synthesis dictionary has strong representation and discrimination capabilities.…”

Section: Introductionmentioning

confidence: 99%

Joint structured constraint discriminant analysis dictionary learning for pattern classification

Zhang

et al. 2023

J. Electron. Imag.

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.Analysis dictionary learning (DL) has been successfully applied to the field of pattern classification. However, it is still a challenge to utilize the local structure information and the class information of samples to improve the discrimination capability of analysis dictionary. We proposed a joint structured constraint discriminant analysis DL (ADL) method (JSCDADL) to learn a structured discriminant analysis dictionary by combining the local structure information and the structured information of samples. Specifically, we first designed an adaptive local structure preserving term (ALSPT) to improve the discrimination capability of analysis dictionary. It adaptively transmits the local structure information of samples to analysis dictionary, which ensures that the same class of samples has similar sparse codes under the action of the analysis dictionary. Then, we designed a discriminative sparse coding error term that forces the coding coefficient matrix to have the desired block diagonal structure. To further enhance the discrimination capability of analysis dictionary, we designed an analysis dictionary combination term by constantly approximating the two analysis dictionaries learned to obtain an analysis dictionary with the local structure information and the structured information of samples. Moreover, we designed an effective iterative algorithm to solve the optimization problem of JSCDADL. Extensive experimental results on six datasets demonstrate that JSCDADL can achieve satisfactory classification performance compared with some state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Joint structured constraint discriminant analysis dictionary learning for pattern classification

Zhang

et al. 2023

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

Self-eliminating Discriminant Analysis Dictionary Learning for Pattern Classification

Cited by 1 publication

References 40 publications

Joint structured constraint discriminant analysis dictionary learning for pattern classification

Joint structured constraint discriminant analysis dictionary learning for pattern classification

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