Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints
نویسندگان
چکیده
منابع مشابه
Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints
Relevant component analysis (RCA) is a recently proposed metric learning method for semi-supervised learning applications. It is a simple and efficient method that has been applied successfully to give impressive results. However, RCA can make use of supervisory information in the form of positive equivalence constraints only. In this paper, we propose an extension to RCA that allows both posit...
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ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2006
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2005.12.004