| Similar component analysis |
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| 作者姓名： | ZHANG Hong WANG Xin LI Junwei CAO Xianguang |
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| 作者单位： | 1. Image Center, Astronautics School， BeiHang University, Beijing 100083, China; 2. The 207th Institute of the Second Academy of China Aerospace Science Industry Corporation, Beijing 100854, China |
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| 基金项目： | Supported by the State Key Laboratory of Object Identity (Grant No .51476010105HK0101) |
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| 摘 要： | A new unsupervised feature extraction method called similar component analysis (SCA) is proposed in this paper. SCA method has a self-aggregation property that the data objects will move towards each other to form clusters through SCA theoretically, which can reveal the inherent pattern of similarity hidden in the dataset. The inputs of SCA are just the pairwise similarities of the dataset, which makes it easier for time series analysis due to the variable length of the time series. Our experimental results on many problems have verified the effectiveness of SCA on some engineering application.
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| 关 键 词： | clustering feature extraction similar component. |
Similar component analysis |
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| ZHANG Hong,WANG Xin,LI Junwei,CAO Xianguang.Similar component analysis[J].Progress in Natural Science,2006,16(12):1343-1347. |
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| Authors: | ZHANG Hong WANG Xin LI Junwei CAO Xianguang |
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| Abstract: | A new unsupervised feature extraction method called similar component analysis (SCA) is proposed in this paper. SCA method has a self-aggregation property that the data objects will move towards each other to form clusters through SCA theoretically, which can reveal the inherent pattern of similarity hidden in the dataset. The inputs of SCA are just the pairwise similarities of the dataset, which makes it easier for time series analysis due to the variable length of the time series. Our experimental results on many problems have verified the effectiveness of SCA on some engineering application. |
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| Keywords: | clustering feature extraction similar component |
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