Detecting outlying subspaces for high-dimensional data: a heuristic search approach
Paper
| Paper/Presentation Title | Detecting outlying subspaces for high-dimensional data: a heuristic search approach |
|---|---|
| Presentation Type | Paper |
| Authors | |
| Author | Zhang, Ji |
| Journal or Proceedings Title | Proceedings of the 2005 SIAM International Workshop on Feature Selection for Data Mining: Interfacing Machine Learning and Statistics |
| Number of Pages | 7 |
| Year | 2005 |
| Web Address (URL) of Paper | http://enpub.fulton.asu.edu/workshop/FSDM05-Proceedings.pdf |
| Conference/Event | 2005 SIAM International Workshop on Feature Selection for Data Mining: Interfacing Machine Learning and Statistics |
| Event Details | 2005 SIAM International Workshop on Feature Selection for Data Mining: Interfacing Machine Learning and Statistics Event Date 23 Apr 2005 Event Location Newport Beach, United States of America |
| Abstract | [Abstract]: In this paper, we identify a new task for studying the out-lying degree of high-dimensional data, i.e. finding the sub-spaces (subset of features) in which given points are out-liers, and propose a novel detection algorithm, called High-D 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 |
| Keywords | outlying subspaces, high-dimensional data, Heuristic search, sample-based learning |
| ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
| Public Notes | No evidence of copyright restrictions. |
| Byline Affiliations | University of Toronto, Canada |
https://research.usq.edu.au/item/9z286/detecting-outlying-subspaces-for-high-dimensional-data-a-heuristic-search-approach
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