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In this paper we propose a two-stage algorithm for robust K-subspaces recovery. In the first stage, a large number of local candidate subspaces are generated by probabilistic farthest insertion, and then the initial near-optimal K-subspaces are solved by combinatorial selection with randomized greedy method. In the second stage, the K-subspaces are further refined by assigning each data vector to...
Expectation-Maximization (EM) is typically used to compute maximum likelihood estimates given incomplete samples and estimated the parameters. We proposed a new algorithm for generating an extension Dynamic Topic Model (exDTM)-in a time-based manner and based on the distribution of documents topics on Spark. The proposed algorithm can be applied in clustering documents from data streams for threat...
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