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Finding the closest pair among a given set of points under Hamming Metric is a fundamental problem with many applications. Let n be the number of points and D the dimensionality of all points. We show that for 0 < D ≤ n0.294, the problem, with the binary alphabet set, can be solved within time complexity $O\left(n^{2+o(1)}\right)$ , whereas for n0.294 < D ≤ n, it can...
Topic modeling is a well-known approach for document analysis. In this paper, we propose a new model, and corresponding optimization algorithm for topic modeling. Experimental results on polarity classification demonstrate that the new model provides a more accurate characterization for document corpus, and archived higher classification accuracy compared to Latent Dirichlet Allocation (LDA).
We consider the problem of recovering a target matrix that is a superposition of low-rank and sparse components, from a small set of linear measurements. This problem arises in compressed sensing of structured high-dimensional signals such as videos and hyperspectral images, as well as in the analysis of transformation invariant low-rank recovery. We analyze the performance of the natural convex heuristic...
In this paper, we study the problem of decomposing a superposition of a low-rank matrix and a sparse matrix when a relatively few linear measurements are available. This problem arises in many data processing tasks such as aligning multiple images or rectifying regular texture, where the goal is to recover a low-rank matrix with a large fraction of corrupted entries in the presence of nonlinear domain...
Image similarity search is a fundamental problem in computer vision. Efficient similarity search across large image databases depends critically on the availability of compact image representations and good data structures for indexing them. Numerous approaches to the problem of generating and indexing image codes have been presented in the literature, but existing schemes generally lack explicit...
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