Learning local metrics from pairwise similarity data

作者：Bohne Julien^*; Ying Yiming; Gentric Stephane; Pontil Massimiliano

来源：Pattern Recognition, 2018, 75: 315-326.

DOI：10.1016/j.patcog.2017.04.002

• Similarity function learning
• Local metric learning
• Nearest neighbors classification
• Face verification

We study the problem of learning a similarity function from a set of binary labeled data pairs. A common approach is to learn a similarly function which is a bilinear form associated to the pair of data points. We argue that this class may be too restrictive when handling heterogeneous datasets. To overcome this limitation local metric learning techniques have been advocated in the literature. However, they are subject to certain constraints preventing their usage in many applications. For example, they require knowledge of the class label of the training points. In this paper, we present a local metric learning method, which overcomes these limitations. The method first initializes a Gaussian mixture model on the training data. Then it estimates a set of local metrics and simultaneously refines the mixture's parameters. Finally, a similarity function is obtained by aggregating the local metrics. We also introduce a novel regularization term, which works well in a transfer learning setting. Our experiments show that the proposed method achieves state-of-the-art results on several real datasets.

• 出版日期2018-3