Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75686
Title: Rotational invariant dimensionality reduction algorithms
Authors: Lai, ZH 
Xu, Y
Yang, J
Shen, LL
Zhang, D 
Keywords: Dimensionality reduction
Image classification
Image feature extraction
Rotational invariant (RI) subspace learning
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on cybernetics, 2017, v. 47, no. 11, p. 3733-3746 How to cite?
Journal: IEEE transactions on cybernetics 
Abstract: A common intrinsic limitation of the traditional sub-space learning methods is the sensitivity to the outliers and the image variations of the object since they use the L-2 norm as the metric. In this paper, a series of methods based on the L-2,L-1-norm are proposed for linear dimensionality reduction. Since the L-2,L-1-norm based objective function is robust to the image variations, the proposed algorithms can perform robust image feature extraction for classification. We use different ideas to design different algorithms and obtain a unified rotational invariant (RI) dimensionality reduction framework, which extends the well-known graph embedding algorithm framework to a more generalized form. We provide the comprehensive analyses to show the essential properties of the proposed algorithm framework. This paper indicates that the optimization problems have global optimal solutions when all the orthogonal projections of the data space are computed and used. Experimental results on popular image datasets indicate that the proposed RI dimensionality reduction algorithms can obtain competitive performance compared with the previous L-2 norm based sub-space learning algorithms.
URI: http://hdl.handle.net/10397/75686
ISSN: 2168-2267
EISSN: 2168-2275
DOI: 10.1109/TCYB.2016.2578642
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