Jointly Learning Data-Dependent Label and Locality-Preserving Projections

Wang, Chang; Mahadevan, Sridhar

doi:10.5591/978-1-57735-516-8/IJCAI11-260

Jointly Learning Data-Dependent Label and Locality-Preserving Projections

Chang Wang, Sridhar Mahadevan

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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1547-1551.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-260

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.

Chang Wang, and Sridhar Mahadevan. 2011. Jointly Learning Data-Dependent Label and Locality-Preserving Projections. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 1547–1551. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-260

@inproceedings{ijcai2011p260,
  title     = {Jointly Learning Data-Dependent Label and Locality-Preserving Projections},
  author    = {Wang, Chang and Mahadevan, Sridhar},
  booktitle = {Proceedings of the Twenty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-11}},
  publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
  editor    = {Toby Walsh},
  pages     = {1547--1551},
  year      = {2011},
  month     = {7},
  doi       = {10.5591/978-1-57735-516-8/IJCAI11-260},
  url       = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-260},
}

TY  - CPAPER
TI  - Jointly Learning Data-Dependent Label and Locality-Preserving Projections
AU  - Wang, Chang
AU  - Mahadevan, Sridhar
T2  - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11
PB  - AAAI Press/International Joint Conferences on Artificial Intelligence
PY  - 2011
SP  - 1547
EP  - 1551
DO  - 10.5591/978-1-57735-516-8/IJCAI11-260
UR  - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-260
ER  -