Two-dimensional supervised local similarity and diversity projection
Two-dimensional supervised local similarity and diversity projection
复制标题
二维监督局部相似性和多样性投影
DOI：
10.1016/j.patcog.2010.05.017
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发表时间：
2010-10
影响因子：
8
通讯作者：
Xu, Hui
中科院分区：
文献类型：
--
作者：
Li, Yi-Ying;Gao, Quan-Xue;Xie, De-Yan;Xu, Hui
This paper presents a novel manifold learning method, namely two-dimensional supervised local similarity and diversity projection (2DSLSDP), for feature extraction. The proposed method defines two weighted adjacency graphs, namely similarity graph and diversity graph. The affinity matrix of similarity graph is determined by the spatial relationship between vertices of this graph, while affinity matrix of diversity graph is determined by the diversity information of vertices of its graph. Using these two graphs, the proposed method constructs local similarity scatter and diversity scatter, respectively. A concise feature extraction criterion is then raised via minimizing the ratio of the local similarity scatter to local diversity scatter. Thus, 2DSLSDP can well preserve not only the adjacency similarity structure, but also the diversity of data points, which is important for the classification. Experiments on the AR and UMIST databases show the effectiveness of the proposed method.
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DOI：
10.1109/tpami.2005.55
发表时间：
2005-03-01
影响因子：
23.6
作者：
He, XF;Yan, SC;Zhang, HJ
通讯作者：
Zhang, HJ
DOI：
10.1109/tpami.2007.250598
发表时间：
2007-01-01
影响因子：
23.6
作者：
Yan, Shuicheng;Xu, Dong;Lin, Stephen
通讯作者：
Lin, Stephen
DOI：
10.1109/iccv.2005.167
发表时间：
2005-10
期刊：
Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
影响因子：
--
作者：
Xiaofei He;Deng Cai;Shuicheng Yan;HongJiang Zhang
通讯作者：
Xiaofei He;Deng Cai;Shuicheng Yan;HongJiang Zhang
DOI：
10.1007/978-1-4302-1609-4_3
发表时间：
2007-07
期刊：
--
影响因子：
--
作者：
Deng Cai;Xiaofei He;Jiawei Han
通讯作者：
Deng Cai;Xiaofei He;Jiawei Han
DOI：
10.1016/j.patcog.2007.07.003
发表时间：
2008
期刊：
Pattern Recognit.
影响因子：
--
作者：
Z. Hu
通讯作者：
Z. Hu