Joint Feature Selection with Low-rank Dictionary Learning

Homa Foroughi, Moein Shakeri, Nilanjan Ray and Hong Zhang

Abstract

Feature selection is one of the well known dimensionality reduction methods that efficiently describes the input data by removing irrelevant variables and reduces the effects of noise to provide good prediction results. In this paper, we propose a feature selection method by integrating dictionary learning and low-rank matrix approximation and apply it to image classification. The objective function finds a subset of features by preserving the reconstructive relationship of the data. This is achieved by minimizing the within-class reconstruction residual and simultaneously maximizing the between-class reconstruction residual. Simultaneously, the l_{2,1}-norm minimization on projection matrix is applied to jointly select the most relevant and discriminative features. The combination of low-rank approximation and Fisher discrimination dictionary learning, leads in more compactness within the same class and dissimilarity between different classes. As a result, even a simple classifier like KNN would perform surprisingly well and classify data accurately. Our proposed method is extensively evaluated on different benchmark image datasets and shows superior performance over several feature selection methods. The experimental results together with the theoretical analysis validate the effectiveness of our method for feature selection, and its efficacy for image classification.

Session

Learning and Recognition

Files

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DOI

10.5244/C.29.97
https://dx.doi.org/10.5244/C.29.97

Citation

Homa Foroughi, Moein Shakeri, Nilanjan Ray and Hong Zhang. Joint Feature Selection with Low-rank Dictionary Learning. In Xianghua Xie, Mark W. Jones, and Gary K. L. Tam, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 97.1-97.13. BMVA Press, September 2015.

Bibtex

@inproceedings{BMVC2015_97,
	title={Joint Feature Selection with Low-rank Dictionary Learning},
	author={Homa Foroughi and Moein Shakeri and Nilanjan Ray and Hong Zhang},
	year={2015},
	month={September},
	pages={97.1-97.13},
	articleno={97},
	numpages={13},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.97},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.97}
}