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Large scale multi-class classification with truncated nuclear norm regularization
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DOI：10.1016/j.neucom.2014.06.073.png)
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In this paper, we consider the problem of multi-class image classification when the classes behaviour has a low rank structure. That is, classes can be embedded into a low dimensional space. Traditional multi-class classification algorithms usually use nuclear norm to approximate the rank of the weight matrix. Considering the limited ability of the nuclear norm for the accurate approximation, we propose a new scalable large scale multi-class classification algorithm by using the recently proposed truncated nuclear norm as a better surrogate of the rank operator of matrices along with multinomial logisitic loss. To solve the non-convex and non-smooth optimization problem, we further develop an efficient iterative procedure. In each iteration, by lifting the non-smooth convex subproblem into an infinite dimensional l(1) norm regularized problem, a simple and efficient accelerated coordinate descent algorithm is applied to find the optimal solution; We conduct a series of evaluations on several public large scale image datasets, where the experimental results show the encouraging improvement of classification accuracy of the proposed algorithm in comparison with the state-of-the-art multi-class classification algorithms. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Truncated nuclear norm
Coordinate descent algorithm
Multi-class classification
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6.5
论文数:
2.5W
被引数:
6.5W
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