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Found 5 papers, 1 papers with code
Provable Inductive Robust PCA via Iterative Hard Thresholding
The robust PCA problem, wherein, given an input data matrix that is the superposition of a low-rank matrix and a sparse matrix, we aim to separate out the low-rank and sparse components, is a well-studied problem in machine learning.
Inductive Pairwise Ranking: Going Beyond the n log(n) Barrier
We study the problem of ranking a set of items from nonactively chosen pairwise preferences where each item has feature information with it.
Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations
Robust tensor CP decomposition involves decomposing a tensor into low rank and sparse components.
Non-convex Robust PCA
In contrast, existing methods for robust PCA, which are based on convex optimization, have $O(m^2n)$ complexity per iteration, and take $O(1/\epsilon)$ iterations, i. e., exponentially more iterations for the same accuracy.
Online Tensor Methods for Learning Latent Variable Models
We introduce an online tensor decomposition based approach for two latent variable modeling problems namely, (1) community detection, in which we learn the latent communities that the social actors in social networks belong to, and (2) topic modeling, in which we infer hidden topics of text articles.
