Generalized Canonical Correlation Analysis for Classification
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2013
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914848839827456 |
|---|---|
| author | Shen, Cencheng Sun, Ming Tang, Minh Priebe, Carey E. |
| author_facet | Shen, Cencheng Sun, Ming Tang, Minh Priebe, Carey E. |
| contents | For multiple multivariate data sets, we derive conditions under which Generalized Canonical Correlation Analysis (GCCA) improves classification performance of the projected datasets, compared to standard Canonical Correlation Analysis (CCA) using only two data sets. We illustrate our theoretical results with simulations and a real data experiment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1304_7981 |
| institution | arXiv |
| publishDate | 2013 |
| record_format | arxiv |
| spellingShingle | Generalized Canonical Correlation Analysis for Classification Shen, Cencheng Sun, Ming Tang, Minh Priebe, Carey E. Machine Learning For multiple multivariate data sets, we derive conditions under which Generalized Canonical Correlation Analysis (GCCA) improves classification performance of the projected datasets, compared to standard Canonical Correlation Analysis (CCA) using only two data sets. We illustrate our theoretical results with simulations and a real data experiment. |
| title | Generalized Canonical Correlation Analysis for Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/1304.7981 |