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In this paper, we propose a novel algorithm based on nonconvex sparsity-inducing penalty for one-bit compressed sensing. We prove that our algorithm has a sample complexity of O(s// 2) for strong signals, and O(s log d// 2) for weak signals , where s is the number of nonzero entries in the signal vector, d is the signal dimension and is the recovery error.(More)
In a collection of documents, such as news articles or tweets, various events take place over time. The event detection problem aims at discovering significant events that have not been mentioned before the detection time. When these events occur, we observe that topic distributions of documents will diverge notably. However, event detection from such(More)
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