Joint Group Sparse PCA for Compressed Hyperspectral Imaging

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Zohaib KhanAjmal Mian

Abstract

A sparse principal component analysis (PCA) seeks a sparse linear combination of input features (variables), so that the derived features still explain most of the variations in the data. A group sparse PCA introduces structural constraints on the features in seeking such a linear combination. Collectively, the derived principal components may still require measuring all the input features. We present a joint group sparse PCA (JGSPCA) algorithm, which forces the basic coefficients corresponding to a group of features to be jointly sparse. Joint sparsity ensures that the complete basis involves only a sparse set of input features, whereas the group sparsity ensures that the structural integrity of the features is maximally preserved. We evaluate the JGSPCA algorithm on the problems of compressed hyperspectral imaging and face recognition. Compressed sensing results show that the proposed method consistently outperforms sparse PCA and group sparse PCA in reconstructing the hyperspectral scenes of natural and man-made objects. The efficacy of the proposed compressed sensing method is further demonstrated in band selection for face recognition.

References

Dec 27, 2008·IEEE Transactions on Pattern Analysis and Machine Intelligence·John WrightYi Ma
Mar 31, 2010·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Fumihito YasumaShree K Nayar
Jan 1, 1991·Journal of Cognitive Neuroscience·M Turk, A Pentland
Jan 1, 2010·Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·Jian GuoJi Zhu

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Citations

Jun 14, 2016·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Zhi-Wei PanJohn H Xin
Jul 19, 2019·Neural Networks : the Official Journal of the International Neural Network Society·Duy Nhat Phan, Hoai An Le Thi

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