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| Multi-Sample Incremental Manifold Learning Algorithm Based on Isogonal Mapping |
| TAN Chao1, GUAN Ji-Hong1, ZHOU Shui-Geng2 |
1.Computer Science and Technology Department, Tongji University, Shanghai 201804
2.Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science,Fudan University, Shanghai 200433 |
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Abstract In the classical dimension reducing manifold learning algorithms, the distance is used to measure the similarity between data, and the problem of subspace deviation caused by noise can not be solved.A multi-sample incremental manifold learning algorithm based on Isogonal mapping is proposed. The covariance matrix of the high dimensional samples with sample mean as the center is turned into the covariance matrix with neighborhood mean as the center. Thus, the error of the subspace caused by distance measurement is eliminated, the covariance matrix is weighted, and the effect of noise or irregular new samples on dimension reduction is reduced. Experimental results show an improvement of the proposed algorithm compared with other algorithms. Moreover, the proposed algorithm can be well applied to image recognition.
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Received: 13 May 2013
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2014, Vol. 27 
