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https://doi.org/10.1109/ICASSP.2008.4518364
DC Field | Value | |
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dc.title | Discriminant simplex analysis | |
dc.contributor.author | Fu, Y. | |
dc.contributor.author | Yan, S. | |
dc.contributor.author | Huang, T.S. | |
dc.date.accessioned | 2014-06-19T03:06:48Z | |
dc.date.available | 2014-06-19T03:06:48Z | |
dc.date.issued | 2008 | |
dc.identifier.citation | Fu, Y.,Yan, S.,Huang, T.S. (2008). Discriminant simplex analysis. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings : 3333-3336. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/ICASSP.2008.4518364" target="_blank">https://doi.org/10.1109/ICASSP.2008.4518364</a> | |
dc.identifier.isbn | 1424414849 | |
dc.identifier.issn | 15206149 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/69978 | |
dc.description.abstract | Image representation and distance metric are both significant for learning-based visual classification. This paper presents the concept of κ-Nearest-Neighbor Simplex (κNNS), which is a simplex with the vertices as the κ nearest neighbors of a certain point. κNNS contributes to the image classification problem in two aspects. First, a novel distance metric between a point to its κNNS within a certain class is provided for general classification problem. Second, we develop a new subspace learning algorithm, called Discriminant Simplex Analysis (DSA), to pursue effective feature representation for image classification. In DSA, the within-locality and between-locality are both modeled by κNNS distance, which provides a more accurate and robust measurement of the probability of a point belonging to a certain class. Experiments on real-world image classification demonstrate the effectiveness of both DSA as well as κNNS based classification approach. ©2008 IEEE. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICASSP.2008.4518364 | |
dc.source | Scopus | |
dc.subject | Discriminant simplex analysis | |
dc.subject | Graph embedding | |
dc.subject | k-nearest-neighbor simplex | |
dc.subject | Subspace learning | |
dc.type | Conference Paper | |
dc.contributor.department | ELECTRICAL & COMPUTER ENGINEERING | |
dc.description.doi | 10.1109/ICASSP.2008.4518364 | |
dc.description.sourcetitle | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | |
dc.description.page | 3333-3336 | |
dc.description.coden | IPROD | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
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