Jointly Learning Data-Dependent Label and Locality-Preserving Projections
Abstract
This paper describes a novel framework to jointly learn data-dependent label and locality-preserving projections. Given a set of data instances from multiple classes, the proposed approach can automatically learn which classes are more similar to each other, and construct discriminative features using both labeled and unlabeled data to map similar classes to similar locations in a lower dimensional space. In contrast to linear discriminant analysis (LDA) and its variants, which can only return c-1 features for a problem with c classes, the proposed approach can generate d features, where d is bounded only by the number of the input features. We describe and evaluate the new approach both theoretically and experimentally, and compare its performance with other state of the art methods.
Cite
Text
Wang and Mahadevan. "Jointly Learning Data-Dependent Label and Locality-Preserving Projections." International Joint Conference on Artificial Intelligence, 2011. doi:10.5591/978-1-57735-516-8/IJCAI11-260Markdown
[Wang and Mahadevan. "Jointly Learning Data-Dependent Label and Locality-Preserving Projections." International Joint Conference on Artificial Intelligence, 2011.](https://mlanthology.org/ijcai/2011/wang2011ijcai-jointly/) doi:10.5591/978-1-57735-516-8/IJCAI11-260BibTeX
@inproceedings{wang2011ijcai-jointly,
title = {{Jointly Learning Data-Dependent Label and Locality-Preserving Projections}},
author = {Wang, Chang and Mahadevan, Sridhar},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2011},
pages = {1547-1552},
doi = {10.5591/978-1-57735-516-8/IJCAI11-260},
url = {https://mlanthology.org/ijcai/2011/wang2011ijcai-jointly/}
}