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Rotational Invariant Dimensionality Reduction Algorithms | IEEE Journals & Magazine | IEEE Xplore

Rotational Invariant Dimensionality Reduction Algorithms


Abstract:

A common intrinsic limitation of the traditional subspace learning methods is the sensitivity to the outliers and the image variations of the object since they use the L2...Show More

Abstract:

A common intrinsic limitation of the traditional subspace learning methods is the sensitivity to the outliers and the image variations of the object since they use the L2 norm as the metric. In this paper, a series of methods based on the L2,1-norm are proposed for linear dimensionality reduction. Since the L2,1-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 L2 norm based subspace learning algorithms.
Published in: IEEE Transactions on Cybernetics ( Volume: 47, Issue: 11, November 2017)
Page(s): 3733 - 3746
Date of Publication: 30 June 2016

ISSN Information:

PubMed ID: 27390196

Funding Agency:


References

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