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» Gene Expression Clustering with Functional Mixture Models
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TIP
2002
98views more  TIP 2002»
13 years 8 months ago
Joint-MAP Bayesian tomographic reconstruction with a gamma-mixture prior
We address the problem of Bayesian image reconstruction with a prior that captures the notion of a clustered intensity histogram. The problem is formulated in the framework of a j...
Ing-Tsung Hsiao, Anand Rangarajan, Gene Gindi
ICCV
2003
IEEE
14 years 11 months ago
Bayesian Clustering of Optical Flow Fields
We present a method for unsupervised learning of classes of motions in video. We project optical flow fields to a complete, orthogonal, a-priori set of basis functions in a probab...
Jesse Hoey, James J. Little
ICANN
2005
Springer
14 years 2 months ago
High-Throughput Multi-dimensional Scaling (HiT-MDS) for cDNA-Array Expression Data
Multidimensional Scaling (MDS) is a powerful dimension reduction technique for embedding high-dimensional data into a lowdimensional target space. Thereby, the distance relationshi...
Marc Strickert, Stefan Teichmann, Nese Sreenivasul...
BMCBI
2007
148views more  BMCBI 2007»
13 years 9 months ago
Evaluation of gene-expression clustering via mutual information distance measure
Background: The definition of a distance measure plays a key role in the evaluation of different clustering solutions of gene expression profiles. In this empirical study we compa...
Ido Priness, Oded Maimon, Irad E. Ben-Gal
NIPS
2004
13 years 10 months ago
Semi-supervised Learning with Penalized Probabilistic Clustering
While clustering is usually an unsupervised operation, there are circumstances in which we believe (with varying degrees of certainty) that items A and B should be assigned to the...
Zhengdong Lu, Todd K. Leen