Learning a Latent Simplex in Input-Sparsity Time
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We consider the problem of learning a latent k-vertex simplex K⊂ℝ^d, given access to A∈ℝ^d× n, which can be viewed as a data matrix with n points that are obtained by randomly perturbing latent points in the simplex K (potentially beyond K). A large class of latent variable models, such as adversarial clustering, mixed membership stochastic block models, and topic models can be cast as learning a latent simplex. Bhattacharyya and Kannan (SODA, 2020) give an algorithm for learning such a latent simplex in time roughly O(k·nnz(A)), where nnz(A) is the number of non-zeros in A. We show that the dependence on k in the running time is unnecessary given a natural assumption about the mass of the top k singular values of A, which holds in many of these applications. Further, we show this assumption is necessary, as otherwise an algorithm for learning a latent simplex would imply an algorithmic breakthrough for spectral low rank approximation. At a high level, Bhattacharyya and Kannan provide an adaptive algorithm that makes k matrix-vector product queries to A and each query is a function of all queries preceding it. Since each matrix-vector product requires nnz(A) time, their overall running time appears unavoidable. Instead, we obtain a low-rank approximation to A in input-sparsity time and show that the column space thus obtained has small sinΘ (angular) distance to the right top-k singular space of A. Our algorithm then selects k points in the low-rank subspace with the largest inner product with k carefully chosen random vectors. By working in the low-rank subspace, we avoid reading the entire matrix in each iteration and thus circumvent the Θ(k·nnz(A)) running time.
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