Substantial progress has been made recently on develo** provably accurate and efficient
algorithms for low-rank matrix factorization via nonconvex optimization. While conventional …

Current research on Internet of Things (IoT) mainly focuses on how to enable general
objects to see, hear, and smell the physical world for themselves, and make them connected …

Recommendation and collaborative filtering systems are important in modern information
and e-commerce applications. As these systems are becoming increasingly popular in …

Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …

Optimization problems with rank constraints arise in many applications, including matrix
regression, structured PCA, matrix completion and matrix decomposition problems. An …

We consider the problem of Robust PCA in the fully and partially observed settings. Without
corruptions, this is the well-known matrix completion problem. From a statistical standpoint …

Error propagation is a general but crucial problem in online semi-supervised video object
segmentation. We aim to suppress error propagation through a correction mechanism with …

The paper studies the problem of recovering a spectrally sparse object from a small number
of time domain samples. Specifically, the object of interest with ambient dimension n is …

This paper studies noisy low-rank matrix completion: given partial and noisy entries of a
large low-rank matrix, the goal is to estimate the underlying matrix faithfully and efficiently …

This paper considers the matrix completion problem. We show that it is not necessary to
assume joint incoherence, which is a standard but unintuitive and restrictive condition that is …