Robust Positive semidefinite L-Isomap Ensemble |
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| Authors: | Fanhua Shang LC JiaoJiarong Shi Jing Chai |
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| Affiliation: | a Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Institute of Intelligent Information Processing, Xidian University, Xi’an 710071, PR China
b Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA |
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| Abstract: | In this paper, we derive an ensemble method inspired by boosting, a novel Robust Positive semidefinite L-Isomap Ensemble (RPL-IsomapE) approach. Specifically, we first apply a constant-shifting method to yield a symmetric positive semidefinite (SPSD) matrix. For topological stability, we also employ a method for eliminating critical outlier points using the confusion rate of all the data points. Then we align individual Robust Positive semidefinite L-Isomap (RPL-Isomap) solutions in common coordinate system through high dimensional affine transformations. Finally, we combine multiple RPL-Isomap solutions by the weighted averaging procedure according to residual variance to improve the noise-robustness of our method. Our RPL-IsomapE maintains the scalability and the speed of L-Isomap. Experiments on two images data sets and a video data set confirm the promising performance of the proposed RPL-IsomapE. |
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| Keywords: | Dimensionality reduction Manifold learning Nyströ m approximation Isomap Ensemble learning High dimensional affine transformation |
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