A Comparative Study on Data Smoothing Regularization for Local Factor Analysis
Refereed conference paper presented and published in conference proceedings

香港中文大學研究人員

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摘要Selecting the cluster number and the hidden factor numbers of Local Factor Analysis (LFA) model is a typical model selection problem, which is difficult when the sample size is finite or small. Data smoothing is one of the three regularization techniques integrated in the statistical learning framework. Bayesian Ying-Yang (BYY) harmony learning theory, to improve parameter learning and model selection. In this paper, we will comparatively investigate the performance of five existing formulas to determine the hyper-parameter namely the smoothing parameter that controls the strength of data smoothing regularization. BYY learning algorithms on LFA using these formulas are evaluated by model selection accuracy on simulated data and classification accuracy on real world data. Two observations are obtained. First, learning with data smoothing works better than that without it especially when sample size is small. Second, the gradient method derived from imposing a sample set based improper prior on the smoothing parameter generally outperforms other methods such as the one from Gamma or Chi-square prior, and the one under the equal covariance priniciple.
著者Tu SK, Shi L, Xu L
會議名稱18th International Conference on Arificial Neural Networks (ICANN 2008)
會議開始日03.09.2008
會議完結日06.09.2008
會議地點Prague
會議國家/地區捷克共和國
期刊名稱Lecture Notes in Artificial Intelligence
詳細描述ed. by V. Kurkov´a et al. .
出版年份2008
月份1
日期1
卷號5163
出版社SPRINGER-VERLAG BERLIN
頁次265 - 274
國際標準書號978-3-540-87535-2
國際標準期刊號0302-9743
語言英式英語
Web of Science 學科類別Computer Science; Computer Science, Theory & Methods

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