Abstract
| Original language | English |
|---|---|
| Title of host publication | Similarity Search and Applications - 12th International Conference, SISAP 2019, Newark, NJ, USA, October 2-4, 2019, Proceedings |
| Editors | Giuseppe Amato, Claudio Gennaro, Vincent Oria, Miloš Radovanovic |
| Publisher | Springer |
| Publication date | 2019 |
| Pages | 281-289 |
| ISBN (Print) | 978-3-030-32046-1 |
| ISBN (Electronic) | 978-3-030-32047-8 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | International Conference on Similarity Search and Applications - New Jersey Institute of Technology (NJIT), Newark, United States Duration: 2. Oct 2019 → 4. Oct 2019 Conference number: 12 http://www.sisap.org/2019/ |
Conference
| Conference | International Conference on Similarity Search and Applications |
|---|---|
| Number | 12 |
| Location | New Jersey Institute of Technology (NJIT) |
| Country | United States |
| City | Newark |
| Period | 02/10/2019 → 04/10/2019 |
| Internet address |
| Series | Lecture Notes in Computer Science |
|---|---|
| Volume | 11807 |
| ISSN | 0302-9743 |
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Keywords
- Estimation
- Intrinsic dimensionality
- Subspace
Cite this
}
Subspace Determination Through Local Intrinsic Dimensional Decomposition. / Becker, Ruben; Hafnaoui, Imane; Houle, Michael E.; Li, Pan; Zimek, Arthur.
Similarity Search and Applications - 12th International Conference, SISAP 2019, Newark, NJ, USA, October 2-4, 2019, Proceedings. ed. / Giuseppe Amato; Claudio Gennaro; Vincent Oria; Miloš Radovanovic. Springer, 2019. p. 281-289 (Lecture Notes in Computer Science, Vol. 11807).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
TY - GEN
T1 - Subspace Determination Through Local Intrinsic Dimensional Decomposition
AU - Becker, Ruben
AU - Hafnaoui, Imane
AU - Houle, Michael E.
AU - Li, Pan
AU - Zimek, Arthur
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Estimation
KW - Intrinsic dimensionality
KW - Subspace
U2 - 10.1007/978-3-030-32047-8_25
DO - 10.1007/978-3-030-32047-8_25
M3 - Article in proceedings
SN - 978-3-030-32046-1
T3 - Lecture Notes in Computer Science
SP - 281
EP - 289
BT - Similarity Search and Applications - 12th International Conference, SISAP 2019, Newark, NJ, USA, October 2-4, 2019, Proceedings
A2 - Amato, Giuseppe
A2 - Gennaro, Claudio
A2 - Oria, Vincent
A2 - Radovanovic, Miloš
PB - Springer
ER -