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» Mixture Models and Frequent Sets: Combining Global and Local...
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TASLP
2010
138views more  TASLP 2010»
12 years 11 months ago
Glimpsing IVA: A Framework for Overcomplete/Complete/Undercomplete Convolutive Source Separation
Abstract--Independent vector analysis (IVA) is a method for separating convolutedly mixed signals that significantly reduces the occurrence of the well-known permutation problem in...
Alireza Masnadi-Shirazi, Wenyi Zhang, Bhaskar D. R...
RECOMB
2009
Springer
14 years 5 months ago
Spatial Clustering of Multivariate Genomic and Epigenomic Information
The combination of fully sequence genomes and new technologies for high density arrays and ultra-rapid sequencing enables the mapping of generegulatory and epigenetics marks on a g...
Rami Jaschek, Amos Tanay
CVPR
2009
IEEE
14 years 12 months ago
Shared Kernel Information Embedding for Discriminative Inference
Latent Variable Models (LVM), like the Shared-GPLVM and the Spectral Latent Variable Model, help mitigate over- fitting when learning discriminative methods from small or modera...
David J. Fleet, Leonid Sigal, Roland Memisevic
TMI
2002
248views more  TMI 2002»
13 years 4 months ago
Adaptive Elastic Segmentation of Brain MRI via Shape Model Guided Evolutionary Programming
This paper presents a fully automated segmentation method for medical images. The goal is to localize and parameterize a variety of types of structure in these images for subsequen...
Alain Pitiot, Arthur W. Toga, Paul M. Thompson