Model multiple heterogeneity via hierarchical multi-latent space learning

In many real world applications such as satellite image analysis, gene function prediction, and insider threat detection, the data collected from heterogeneous sources often exhibit multiple types of heterogeneity, such as task heterogeneity, view heterogeneity, and label heterogeneity. To address this problem, we propose a Hierarchical Multi-Latent Space (HiMLS) learning approach to jointly model the triple types of heterogeneity. The basic idea is to learn a hierarchical multi-latent space by which we can simultaneously leverage the task relatedness, view consistency and the label correlations to improve the learning performance. We first propose a multi-latent space framework to model the complex heterogeneity, which is used as a building block to stack up a multi-layer structure so as to learn the hierarchical multilatent space. In such a way, we can gradually learn the more abstract concepts in the higher level. Then, a deep learning algorithm is proposed to solve the optimization problem. The experimental results on various data sets show the effectiveness of the proposed approach.