Outlier Cluster Formation in Spectral Clustering

TL;DR

This paper reveals intrinsic properties of spectral clustering related to outlier formation and proposes a new evaluation function, demonstrating superior outlier detection and cluster estimation in face clustering and person re-identification tasks.

Contribution

It introduces a novel mathematical insight into spectral clustering's outlier behavior and a new evaluation function, improving outlier detection and cluster number estimation.

Findings

Effective outlier detection in face clustering and re-identification

Accurate estimation of the number of clusters

Outperforms state-of-the-art methods in tested scenarios

Abstract

Outlier detection and cluster number estimation is an important issue for clustering real data. This paper focuses on spectral clustering, a time-tested clustering method, and reveals its important properties related to outliers. The highlights of this paper are the following two mathematical observations: first, spectral clustering's intrinsic property of an outlier cluster formation, and second, the singularity of an outlier cluster with a valid cluster number. Based on these observations, we designed a function that evaluates clustering and outlier detection results. In experiments, we prepared two scenarios, face clustering in photo album and person re-identification in a camera network. We confirmed that the proposed method detects outliers and estimates the number of clusters properly in both problems. Our method outperforms state-of-the-art methods in both the 128-dimensional sparse space for face clustering and the 4,096-dimensional non-sparse space for person re-identification.