Oriented soft localized subspace classification
نویسندگان
چکیده
Subspace methods of pattern recognition form an interesting and popular classiication paradigm. The earliest sub-space method of classiication was the CLass Featuring Information Compression (CLAFIC) which associated with each class a corresponding linear subspace. Local subspace classiication methodologies which have enhanced classii-cation power by associating more than one linear subspace with each class have also been investigated. In this paper we introduce the Oriented Soft Regional Subspace Classiier (OS-RSC). The highlights of this classiication methodology are (i) Class speciic subspaces are formed such that they speciically maximize average projection of one class while minimizing the average projection of the rival class (ii) Multiple manifolds are formed for each class which gives the classiier greater classiication power (iii) a soft sharing of the training patterns again allows for better classiication performance. The performance of the proposed classiier is tested on real-world classiication problems. Also, it turns out that for the cost function under consideration (that forms class speciic subspaces) the maxima is achieved for a subspace of unit dimensionality. This simpliies the clas-siier structure.
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