General subspace learning with corrupted training data via graph embedding
We address the following subspace learning problem: supposing we are given a set of labeled, corrupted training data points, how to learn the underlying subspace, which contains three components: an intrinsic subspace that captures certain desired properties of a data set, a penalty subspace that fi...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 22(2013), 11 vom: 16. Nov., Seite 4380-93
|
|---|
| 1. Verfasser: |
Bao, Bing-Kun
(VerfasserIn) |
|---|
| Weitere Verfasser: |
Liu, Guangcan,
Hong, Richang,
Yan, Shuicheng,
Xu, Changsheng |
|---|
| Format: | Online-Aufsatz
|
|---|
| Sprache: | English |
|---|
| Veröffentlicht: |
2013
|
|---|
| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|
|---|
| Schlagworte: | Journal Article
Research Support, Non-U.S. Gov't |
|---|