Abstract
Manifold learning algorithms have been widely used in data mining and pattern recognition. Despite their attractive properties, most manifold learning algorithms are not robust to outliers. In this paper, a novel outlier detection method for robust manifold learning is proposed. First, the contextual distance based reliability score is proposed to measure the likelihood of each sample to be a clean sample or an outlier. Second, we design an iterative scheme on the reliability score matrix to detect outliers. By considering both local and global manifold structure, the proposed method is more topologically stable than RPCA method. The proposed method can serve as a preprocessing procedure for manifold learning algorithms and make them more robust, as observed from our experimental results.
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Du, C., Sun, J., Zhou, S., Zhao, J. (2013). An Outlier Detection Method for Robust Manifold Learning. In: Yin, Z., Pan, L., Fang, X. (eds) Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013. Advances in Intelligent Systems and Computing, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37502-6_43
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DOI: https://doi.org/10.1007/978-3-642-37502-6_43
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