a fast logdet divergence based metric learning algorithm for large data sets classification
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ID: 224576
2014
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Abstract
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.
| Reference Key |
mei2014abstracta
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|---|---|
| Authors | ;Jiangyuan Mei;Jian Hou;Jicheng Chen;Hamid Reza Karimi |
| Journal | science and technology of advanced materials |
| Year | 2014 |
| DOI |
10.1155/2014/463981
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| URL | |
| Keywords |
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