Support vector data description (SVDD) characterizes a dataset by a spherically shaped boundary around it. Since the complexity of SVDD training is O(N3), its performance decreases for large-scale datasets. In this paper, we propose an improved SVDD algorithm, called EM-SVDD, which combines the expectation maximization (EM) algorithm and SVDD to reduce the complexity and accelerate the training phase, while the accuracy of the classifier remains unchanged. First, the dataset is clustered to obtain smaller subsets, and then the boundary of each subset is identified by SVDD. After that, to construct the dataset boundary and get the optimal weighted combination of SVDDs, the EM algorithm is utilized to estimate the parameters and weights of SVDDs. The time complexity of the proposed method is N/i times lower than SVDD, where i is the number of EM iterations. In addition to EM-SVDD, Sparse EM-SVDD is proposed to guarantee the sparsity of the iteratively estimated parameters. EM-SVDD is well compared with several similar methods. Simulation results indicate higher speed and performance of the proposed method in the training and testing phases. Furthermore, the capability of the proposed method is tested on a large image dataset acquired from social networks and our method identifies in-class and outlier images with 0.71 accuracy rate.

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