Semi-Supervised Discriminant Analysis Based On Data Structure
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2015, Vol 17, Issue 3
Abstract
Abstract: Dimensionality reduction is a key data-analytic technique for mining high-dimensional data. In thispaper, we consider a general problem of learning from pairwise constraints in the form of must-link and cannotlink.As one kind of side information, the must-link constraints imply that a pair of instances belongs to the sameclass, while the cannot-link constraints compel them to be different classes. Given must-link and cannot-linkinformation, the goal of this paper is learn a smooth and discriminative subspace. Specifically, in order togenerate such a subspace, we use pairwise constraints to present an optimization problem, in which a leastsquares formulation that integrates both global and local structures is considered as a regularization term fordimensionality reduction. Experimental results on benchmark data sets show the effectiveness of the proposedalgorithm
Authors and Affiliations
Xuesong Yin , Rongrong Jiang, Lifei Jiang
Keywords
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