Learning low-rank output kernels
2011
Conference Paper
ei
Output kernel learning techniques allow to simultaneously learn a vector-valued function and a positive semidefinite matrix which describes the relationships between the outputs. In this paper, we introduce a new formulation that imposes a low-rank constraint on the output kernel and operates directly on a factor of the kernel matrix. First, we investigate the connection between output kernel learning and a regularization problem for an architecture with two layers. Then, we show that a variety of methods such as nuclear norm regularized regression, reduced-rank regression, principal component analysis, and low rank matrix approximation can be seen as special cases of the output kernel learning framework. Finally, we introduce a block coordinate descent strategy for learning low-rank output kernels.
Author(s): | Dinuzzo, F. and Fukumizu, K. |
Book Title: | JMLR Workshop and Conference Proceedings Volume 20 |
Pages: | 181-196 |
Year: | 2011 |
Month: | November |
Day: | 0 |
Editors: | Hsu, C.-N. , W.S. Lee |
Publisher: | JMLR |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | 3rd Asian Conference on Machine Learning (ACML 2011) |
Event Place: | Taoyuan, Taiwan |
Address: | Cambridge, MA, USA |
Digital: | 0 |
Links: |
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Web |
BibTex @inproceedings{DinuzzoF2011, title = {Learning low-rank output kernels}, author = {Dinuzzo, F. and Fukumizu, K.}, booktitle = {JMLR Workshop and Conference Proceedings Volume 20}, pages = {181-196}, editors = {Hsu, C.-N. , W.S. Lee}, publisher = {JMLR}, address = {Cambridge, MA, USA}, month = nov, year = {2011}, doi = {}, month_numeric = {11} } |