activity
20122016
most citedLow-rank optimization for distance matrix completion

59 citations · 63 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2017

A two-dimensional decomposition approach for matrix completion through gossip

Mukul Bhutani, Bamdev Mishra

Factoring a matrix into two low rank matrices is at the heart of many problems. The problem of matrix completion especially uses it to decompose a sparse matrix into two non sparse…

cs.IT2016

A Sparse and Low-Rank Optimization Framework for Index Coding via Riemannian Optimization

Yuanming Shi, Bamdev Mishra

Side information provides a pivotal role for message delivery in many communication scenarios to accommodate increasingly large data sets, e.g., caching networks. Although index co…

cs.CV2015

Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization

Yanfeng Sun, Junbin Gao, Xia Hong +2

Tensors or multiarray data are generalizations of matrices. Tensor clustering has become a very important research topic due to the intrinsically rich structures in real-world mult…

math.OC201359 cited

Low-rank optimization for distance matrix completion

B. Mishra, G. Meyer, R. Sepulchre

This paper addresses the problem of low-rank distance matrix completion. This problem amounts to recover the missing entries of a distance matrix when the dimension of the data emb…

cs.LG20124 cited

Fixed-rank matrix factorizations and Riemannian low-rank optimization

B. Mishra, G. Meyer, S. Bonnabel +1

Motivated by the problem of learning a linear regression model whose parameter is a large fixed-rank non-symmetric matrix, we consider the optimization of a smooth cost function de…