Kernel method for matrix completion with side information and its application in multi-label learning

Material of “ Speedup Matrix Completion with Side Information : Application to Multi - Label Learning ”

Speedup Matrix Completion with Side Information: Application to Multi-Label Learning

In standard matrix completion theory, it is required to have at least O(n ln n) observed entries to perfectly recover a low-rank matrix M of size n × n, leading to a large number of observations when n is large. In many real tasks, side information in addition to the observed entries is often available. In this work, we develop a novel theory of matrix completion that explicitly explore the sid...

Multi-View Matrix Completion for Clustering with Side Information

In many clustering applications, real world data are often collected from multiple sources or with features from multiple channels. Thus, multi-view clustering has attracted much attention during the past few years. It is noteworthy that in many situations, in addition to the data samples, there is some side information describing the relation between instances, such as must-links and cannot-li...

Matrix Completion with Noisy Side Information

We study the matrix completion problem with side information. Side information has been considered in several matrix completion applications, and has been empirically shown to be useful in many cases. Recently, researchers studied the effect of side information for matrix completion from a theoretical viewpoint, showing that sample complexity can be significantly reduced given completely clean ...

High Rank Matrix Completion with Side Information

We address the problem of high-rank matrix completion with side information. In contrast to existing work dealing with side information, which assume that the data matrix is low-rank, we consider the more general scenario where the columns of the data matrix are drawn from a union of lowdimensional subspaces, which can lead to a high rank matrix. Our goal is to complete the matrix while taking ...