The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Semi-supervised Discriminant Analysis
Abstract
Linear Discriminant Analysis (LDA) has been a popular method for extracting features which preserve class separability. The projection vectors are commonly obtained by maximizing the between class covariance and simultaneously minimizing the within class covariance. In practice, when there is no sufficient training samples, the covariance matrix of each class may not be accurately estimated. In this paper, we propose a novel method, called Semi- supervised Discriminant Analysis (SDA), which makes use of both labeled and unlabeled samples. The labeled data points are used to maximize the separability between different classes and the unlabeled data points are used to estimate the intrinsic geometric structure of the data. Specifically, we aim to learn a discriminant function which is as smooth as possible on the data manifold. Experimental results on single training image face recognition and relevance feedback image retrieval demonstrate the effectiveness of our algorithm.
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Moradi, E., Pepe, A., Gaser, C., Huttunen, H., & Tohka, J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.002 · Cited by 708
Bregman Divergence-Based Regularization for Transfer Subspace Learning
Si, S., Tao, D., & Geng, B. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.126 · Cited by 477
Robust Structured Subspace Learning for Data Representation
Li, Z., Liu, J., Tang, J., & Lu, H. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2400461 · Cited by 353
Feature selection with multi-view data: A survey
Zhang, R., Nie, F., Li, X., & Wei, X. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.11.019 · Cited by 313
Semi-supervised Learning by Sparse Representation
Yan, S., & Wang, H. (2009) · Proceedings of the 2009 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972795.68 · Cited by 266
Metadata
- Authors
- Cai, D., He, X., & Han, J.
- Venue
- 2007 IEEE 11th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2007
- Pages
- 1-7
- OpenAlex
- W2163584563
- Cited by
- 758
- References
- 27
- FWCI
- 32.02
- Type
- conference paper
- Language
- en
- Open access
- closed
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