Subspace Determination Through Local Intrinsic Dimensional Decomposition

Ruben Becker, Imane Hafnaoui, Michael E. Houle, Pan Li, Arthur Zimek

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Abstrakt

Axis-aligned subspace clustering generally entails searching through enormous numbers of subspaces (feature combinations) and evaluation of cluster quality within each subspace. In this paper, we tackle the problem of identifying subsets of features with the most significant contribution to the formation of the local neighborhood surrounding a given data point. For each point, the recently-proposed Local Intrinsic Dimension (LID) model is used in identifying the axis directions along which features have the greatest local discriminability, or equivalently, the fewest number of components of LID that capture the local complexity of the data. In this paper, we develop an estimator of LID along axis projections, and provide preliminary evidence that this LID decomposition can indicate axis-aligned data subspaces that support the formation of clusters.
OriginalsprogEngelsk
TitelSimilarity Search and Applications - 12th International Conference, SISAP 2019, Newark, NJ, USA, October 2-4, 2019, Proceedings
RedaktørerGiuseppe Amato, Claudio Gennaro, Vincent Oria, Miloš Radovanovic
ForlagSpringer
Publikationsdato2019
Sider281-289
ISBN (Trykt)978-3-030-32046-1
ISBN (Elektronisk)978-3-030-32047-8
DOI
StatusUdgivet - 2019
BegivenhedInternational Conference on Similarity Search and Applications - New Jersey Institute of Technology (NJIT), Newark, USA
Varighed: 2. okt. 20194. okt. 2019
Konferencens nummer: 12
http://www.sisap.org/2019/

Konference

KonferenceInternational Conference on Similarity Search and Applications
Nummer12
LokationNew Jersey Institute of Technology (NJIT)
Land/OmrådeUSA
ByNewark
Periode02/10/201904/10/2019
Internetadresse
NavnLecture Notes in Computer Science
Vol/bind11807
ISSN0302-9743

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