Locality preserving discriminative dictionary learning

Haghiri, Siavash; Rabiee, Hamid R.; Soltani-Farani, Ali; Hosseini, Seyed Abbas; Shadloo, Maryam

doi:10.1109/icip.2014.7026061

Cited by 9 publications

(1 citation statement)

References 19 publications

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“…For example, to maintain the structural characteristics of the training samples, Zheng et al [25] proposed a Graph-SC algorithm to generate a Laplace graph of the training samples and used it to design the discriminant of the dictionary. Haghiri et al [26] also proposed a discriminative dictionary learning method that preserves the local structure of the training samples. Zhang et al [27] proposed the Locality-Constrained Projective Dictionary Learning (LC-PDL) algorithm, which adds the local constraint of atoms to maintain local information.…”

Section: Introductionmentioning

confidence: 99%

A Dictionary Learning Algorithm Based on Dictionary Reconstruction and Its Application in Face Recognition

Zheng

Zhang

Liu

et al. 2020

Mathematical Problems in Engineering

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In recent years, dictionary learning has received more and more attention in the study of face recognition. However, most dictionary learning algorithms directly use the original training samples to learn the dictionary, ignoring noise existing in the training samples. For example, there are differences between different images of the same subject due to changes in illumination, expression, etc. To address the above problems, this paper proposes the dictionary relearning algorithm (DRLA) based on locality constraint and label embedding, which can effectively reduce the influence of noise on the dictionary learning algorithm. In our proposed dictionary learning algorithm, first, the initial dictionary and coding coefficient matrix are directly obtained from the training samples, and then the original training samples are reconstructed by the product of the initial dictionary and coding coefficient matrix. Finally, the dictionary learning algorithm is reapplied to obtain a new dictionary and coding coefficient matrix, and the newly obtained dictionary and coding coefficient matrix are used for subsequent image classification. The dictionary reconstruction method can partially eliminate noise in the original training samples. Therefore, the proposed algorithm can obtain more robust classification results. The experimental results demonstrate that the proposed algorithm performs better in recognition accuracy than some state-of-the-art algorithms.

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Section: Introductionmentioning

confidence: 99%