Inverting a Matrix Function around a Singularity via Local Rank Factorization
نویسندگان
چکیده
منابع مشابه
Multi-View Spectral Clustering via Structured Low-Rank Matrix Factorization
Multi-view data clustering attracts more attention than their single view counterparts due to the fact that leveraging multiple independent and complementary information from multi-view feature spaces outperforms the single one. Multi-view Spectral Clustering aims at yielding the data partition agreement over their local manifold structures by seeking eigenvalue-eigenvector decompositions. Amon...
متن کاملNonconvex Low Rank Matrix Factorization via Inexact First Order Oracle
We study the low rank matrix factorization problem via nonconvex optimization. Compared with the convex relaxation approach, nonconvex optimization exhibits superior empirical performance for large scale low rank matrix estimation. However, the understanding of its theoretical guarantees is limited. To bridge this gap, we exploit the notion of inexact first order oracle, which naturally appears...
متن کاملHierarchical community detection via rank-2 symmetric nonnegative matrix factorization
Background Community discovery is an important task for revealing structures in large networks. The massive size of contemporary social networks poses a tremendous challenge to the scalability of traditional graph clustering algorithms and the evaluation of discovered communities. Methods We propose a divide-and-conquer strategy to discover hierarchical community structure, nonoverlapping wit...
متن کاملLearning Structured Low-Rank Representation via Matrix Factorization
A vast body of recent works in the literature have shown that exploring structures beyond data lowrankness can boost the performance of subspace clustering methods such as Low-Rank Representation (LRR). It has also been well recognized that the matrix factorization framework might offer more flexibility on pursuing underlying structures of the data. In this paper, we propose to learn structured...
متن کاملNonnegative Rank Factorization via Rank Reduction
Abstract. Any given nonnegative matrix A ∈ R can be expressed as the product A = UV for some nonnegative matrices U ∈ R and V ∈ R with k ≤ min{m, n}. The smallest k that makes this factorization possible is called the nonnegative rank of A. Computing the exact nonnegative rank and the corresponding factorization are known to be NP-hard. Even if the nonnegative rank is known a priori, no simple ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SIAM Journal on Matrix Analysis and Applications
سال: 2016
ISSN: 0895-4798,1095-7162
DOI: 10.1137/140999839