| Geodesic Distance for Support Vector Machines |
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| 引用本文： | QUAN Yong YANG Jie (Institute of Image Processing and Pattern Recognition,Shanghai Jiaotong University,Shanghai 200030). Geodesic Distance for Support Vector Machines[J]. 自动化学报, 2005, 0(2) |
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| 作者姓名： | QUAN Yong YANG Jie (Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030) |
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| 作者单位： | Institute of Image Processing and Pattern Recognition，Shanghai Jiaotong University，Shanghai 200030 |
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| 基金项目： | Supported by National Natural Science Foundation of P. R. China (50174038, 30170274) |
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| 摘 要： | When dealing with pattern recognition problems one encounters different types of prior knowledge. It is important to incorporate such knowledge into classification method at hand. A very common type of prior knowledge is many data sets are on some kinds of manifolds. Distance based classification methods can make use of this by a modified distance measure called geodesic distance. We introduce a new kind of kernels for support vector machines which incorporate geodesic distance and therefore are applicable in cases such transformation invariance is known. Experiments results show that the performance of our method is comparable to that of other state-of-the-art method.
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Geodesic Distance for Support Vector Machines |
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| QUAN Yong YANG Jie. Geodesic Distance for Support Vector Machines[J]. Acta Automatica Sinica, 2005, 0(2) |
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| Authors: | QUAN Yong YANG Jie |
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| Abstract: | When dealing with pattern recognition problems one encounters different types of prior knowledge. It is important to incorporate such knowledge into classification method at hand. A very common type of prior knowledge is many data sets are on some kinds of manifolds. Distance based classification methods can make use of this by a modified distance measure called geodesic distance. We introduce a new kind of kernels for support vector machines which incorporate geodesic distance and therefore are applicable in cases such transformation invariance is known. Experiments results show that the performance of our method is comparable to that of other state-of-the-art method. |
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| Keywords: | Support vector machine geodesic distance kernel function |
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