Enriching the random subspace method with margin theory – a solution for the high-dimensional classification task
The random subspace method (RSM) has proved its excellence in numbers of pattern recognition tasks. However, the standard RSM is limited owing to the randomness in its feature selection procedure that is likely to lead to feature subset having poor class separability. In this paper, a proposal for a...
Saved in:
| Main Authors: | Hongyan Xu, Tao Lin, Yingtao Xie, Zhi Chen |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2018-10-01
|
| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2018.1512556 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Tensioned Multi-View Ordered Kernel Subspace Clustering
by: Liping Chen, et al.
Published: (2025-06-01) -
Infinite dimensional linear groups with a spacious family of $G$-invariant subspaces
by: A. V. Sadovnichenko
Published: (2013-10-01) -
Enhanced Subspace Iteration Technique for Probabilistic Modal Analysis of Statically Indeterminate Structures
by: Hongfei Cao, et al.
Published: (2024-11-01) -
High Density Subspace Clustering Algorithm for High Dimensional Data
by: WAN Jing, et al.
Published: (2020-08-01) -
Some properties of prereflexive subspaces of operators
by: Jiankui Li
Published: (1998-01-01)