Detecting Outlying Subspaces for High-Dimensional Data: A Heuristic Search Approach

In this paper, we identify a new task for studying the outlying degree of high-dimensional data, i.e. finding the subspaces (subset of features) in which given points are outliers, and propose a novel detection algorithm, called HighD Outlying subspace Detection (HighDOD). We measure the outlying degree of the point using the sum of distances between this point and its k nearest neighbors. Heuristic pruning strategies are proposed to realize fast pruning in the subspace search and an efficient dynamic subspace search method with a sample-based learning process has been implemented. Experimental results show that HighDOD is efficient and outperforms other searching alternatives such as the naive top-down, bottom-up and random search meth-