Outlier Detection in the Framework of Dimensionality Reduction
Qiang Ye, Weifeng Zhi · International Journal of Pattern Recognition and Artificial Intelligence · 2015
We propose an effective outlier detection algorithm for high-dimensional data. We consider manifold models of data as is typically assumed in dimensionality reduction/manifold learning. Namely, we consider a noisy data set sampled from a low-dimensional manifold in a high-dimensional data space. Our algorithm uses local geometric structure to determine inliers, from which the outliers are identified. The algorithm is applicable to both linear and nonlinear models of data. We also discuss various implementation issues and we present several examples to demonstrate the effectiveness of the new approach.