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
Download files
2528
total views216
total downloads0
views this month0
downloads this month