Multi-View Low-Rank Analysis for Outlier Detection

Abstract

Outlier detection is a fundamental problem in data mining. Unlike most existing methods that are designed for single-view data, we propose a multi-view outlier detection approach in this paper. Multi-view data can provide plentiful information of samples, however, detecting outliers from multi-view data is still a challenging problem due to the complicated distribution and inconsistent behavior of samples across different views. We address this problem through robust data representation, by building a Multi-view Low-Rank Analysis (MLRA) framework. Our framework contains two major components. First, it performs cross-view low-rank analysis for revealing the intrinsic structures of data. Second, it identifies outliers by estimating the outlier score for each test sample. Specifically, we formulate the cross-view low-rank analysis as a constrained rank-minimization problem, and present an efficient optimization algorithm to solve it. Different from the existing multi-view outlier detection methods, our framework is able to detect two different types of outliers from multiple views simultaneously. To this end, we design a criterion to estimate the outlier scores by analyzing the obtained representation coefficients. Experimental results on seven UCI datasets and the USPS-MNIST dataset demonstrate that our approach outperforms several state-of-the-art single-view and multi-view outlier detection methods in most cases.