Abstract Support vector data description (SVDD) is an unsupervised learning method with significant application in image recognition and information security. Coordinate descent is an effective method for large-scale classification problems with simple operation and high convergence speed. In this paper, an efficient coordinate descent algorithm for solving large-scale SVDD is presented. The solution of concerned sub-problem at each iteration is derived in closed form and the computational cost is decreased through the accelerating strategy and cheap computation. Meanwhile, three methods for selecting sub-problem, analyzing and comparing their advantage and disadvantage are developed. The experiments on simulation and real large-scale database validate the performance of the proposed algorithm. Compared with LibSVDD, the proposed algorithm has great superiority which takes less than 1.4 seconds to solve a text database from ijcnn with 105 training examples.
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2012, Vol. 25 
