Multi-tensor completion with shared factors from multiple\\ sources

Multi-tensor Completion with Common Structures

In multi-data learning, it is usually assumed that common latent factors exist among multi-datasets, but it may lead to deteriorated performance when datasets are heterogeneous and unbalanced. In this paper, we propose a novel common structure for multi-data learning. Instead of common latent factors, we assume that datasets share Common Adjacency Graph (CAG) structure, which is more robust to ...

Tensor Completion

The purpose of this thesis is to explore the methods to solve the tensor completion problem. Inspired by the matrix completion problem, the tensor completion problem is formulated as an unconstrained nonlinear optimization problem, which finds three factors that give a low-rank approximation. Various of iterative methods, including the gradient-based methods, stochastic gradient descent method ...

From Stochastic Completion Fields to Tensor Voting

Several image processing algorithms imitate the lateral interaction of neurons in the visual striate cortex V1 to account for the correlations along contours and lines. Here we focus on two methodologies: tensor voting by Guy and Medioni, and stochastic completion fields by Mumford, Williams and Jacobs. The objective of this article is to compare these two methods and to place them into a commo...

Multi-Component Efficiency with Shared Resources in Commercial Banks

Multi-Transfer: Transfer Learning with Multiple Views and Multiple Sources

Transfer learning, which aims to help the learning task in a target domain by leveraging knowledge from auxiliary domains, has been demonstrated to be effective in different applications, e.g., text mining, sentiment analysis, etc. In addition, in many real-world applications, auxiliary data are described from multiple perspectives and usually carried by multiple sources. For example, to help c...