Outlier Cluster Formation in Spectral Clustering
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 observ...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
02.03.2017
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.1703.01028 |