A fast Logdet divergence based metric learning algorithm for large data sets classification
Journal article, Peer reviewed
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Original versionMei, J., Hou, J., Chen, J., & Karimi, H. R. (2014). A fast Logdet divergence based metric learning algorithm for large data sets classification. Abstract and Applied Analysis, 2014, 1-9. doi: 10.1155/2014/463981 10.1155/2014/463981
Large data sets classification is widely used in many industrial applications. It is a challenging task to classify large data sets efficiently, accurately, and robustly, as large data sets always contain numerous instances with high dimensional feature space. In order to deal with this problem, in this paper we present an online Logdet divergence based metric learning (LDML) model by making use of the powerfulness of metric learning. We firstly generate a Mahalanobis matrix via learning the training data with LDML model. Meanwhile, we propose a compressed representation for high dimensional Mahalanobis matrix to reduce the computation complexity in each iteration. The final Mahalanobis matrix obtained this way measures the distances between instances accurately and serves as the basis of classifiers, for example, the k-nearest neighbors classifier. Experiments on benchmark data sets demonstrate that the proposed algorithm compares favorably with the state-of-the-art methods.
Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2014/463981 Open Access