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In this paper, we address the Bayesian classification with incomplete data. The common approach in the literature is to simply ignore the samples with missing values or impute missing values before classification. However, these methods are not effective when a large portion of the data have missing values and the acquisition of samples is expensive.(More)
This study presents a novel approach to unsupervised learning for clustering with missing data. We first extend a finite mixture model to the infinite case by considering Dirichlet process mixtures, which can automatically determine the number of mixture components or clusters. Furthermore, we view the missing features as latent variables and compute the(More)
  • Diwen Shen, Daniel G Chatman, Daniel Chatman, Calanit Kamala, Zheng Zhang, Ruoying Xu +15 others
  • 2015
The 2007 Beijing Public Transit Fare Reform likely resulted in high crowding and poor airconditioning provision on transit in Beijing. This paper explores how crowding and thermal comfort affect commuters' travel mode choice using both revealed preference and stated preference approaches. Through an intercept survey, I collected travel data and both(More)
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