Abstract
This paper studied PCA mixture model in high dimensional space. A novel EM learning approach by using perturbation was proposed for the PCA mixture model. Experiments showed the novel perturbation EM algorithm is more effective in learning PCA mixture model than an existing constrained EM algorithm.
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© 2004 Springer-Verlag Berlin Heidelberg
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Jin, Z., Davoine, F., Lou, Z. (2004). An Effective EM Algorithm for PCA Mixture Model. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2004. Lecture Notes in Computer Science, vol 3138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27868-9_68
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DOI: https://doi.org/10.1007/978-3-540-27868-9_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22570-6
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