Efficiently Learning the Metric with Side-Information

Abstract. A crucial problem in machine learning is to choose an appropriate representation of data, in a way that emphasizes the relations we are interested in. In many cases this amounts to finding a suitable metric in the data space. In the supervised case, Linear Discriminant Analysis (LDA) can be used to find an appropriate subspace in which the data structure is apparent. Other ways to learn a suitable metric are found in [6] and [11]. However recently significant attention has been devoted to the problem of learning a metric in the semi-supervised case. In particular the work by Xing et al. [15] has demonstrated how semi-definite programming (SDP) can be used to directly learn a distance measure that satisfies constraints in the form of side-information. They obtain a significant increase in clustering performance with the new representation. The approach is very interesting, however, the computational complexity of the method severely limits its applicability to real machine lea...

• Type: Conference
• Year: 2003
• Where: ALT
• Authors: Tijl De Bie, Michinari Momma, Nello Cristianini
• Keywords: ALT 2003, Linear Discriminant Analysis, Machine Learning, Suitable Metric