Bayesian principal component analysis (BPCA), a probabilistic reformulation of PCA with Bayesian model selection, is a systematic approach to determining the number of essential p...
A random variable with distribution P is observed in Gaussian noise and is estimated by a minimum meansquare estimator that assumes that the distribution is Q. This paper shows tha...
Discovering causal relations among observed variables in a given data set is a main topic in studies of statistics and artificial intelligence. Recently, some techniques to disco...
This paper proposes a new affine registration algorithm
for matching two point sets in IR2 or IR3. The input point
sets are represented as probability density functions, using
e...
Jeffrey Ho, Adrian Peter, Anand Rangarajan, Ming-H...
High dimensional directional data is becoming increasingly important in contemporary applications such as analysis of text and gene-expression data. A natural model for multivaria...
Arindam Banerjee, Inderjit S. Dhillon, Joydeep Gho...