Published 2002
| Published
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Grouping and dimensionality reduction by locally linear embedding
- Creators
- Polito, Marzia
-
Perona, Pietro
Chicago
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Abstract
Locally Linear Embedding (LLE) is an elegant nonlinear dimensionality-reduction technique recently introduced by Roweis and Saul 2]. It fails when the data is divided into separate groups. We study a variant of LLE that can simultaneously group the data and calculate local embedding of each group. An estimate for the upper bound on the intrinsic dimension of the data set is obtained automatically.
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Additional details
- Eprint ID
- 47618
- Resolver ID
- CaltechAUTHORS:20140730-101719764
- Created
-
2014-08-19Created from EPrint's datestamp field
- Updated
-
2019-10-03Created from EPrint's last_modified field
- Series Name
- Advances in Neural Information Processing Systems
- Series Volume or Issue Number
- 2