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In a standard support vector machine (SVM), the training process has O(n3) time and O(n2) space complexities, where n is the size of training dataset. Thus, it is computationally infeasible for very large datasets. Reducing the size of training dataset is naturally considered to solve this problem. SVM classifiers depend on only support vectors (SVs) that lie close to the separation boundary. Therefore,...
Soft subspace clustering algorithms receive wide interests recently, because of their scalable and flexible ability at handling high dimensional sparse data. A disadvantage of those existing algorithms is their clustering results are affected by goodness of initial centroid selected by random initial method greatly. In this paper, we propose a heuristically weighting K-means algorithm and a corresponding...
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