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» Finding Clusters and Components by Unsupervised Learning
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NIPS
2008
14 years 11 months ago
Adaptive Template Matching with Shift-Invariant Semi-NMF
How does one extract unknown but stereotypical events that are linearly superimposed within a signal with variable latencies and variable amplitudes? One could think of using temp...
Jonathan Le Roux, Alain de Cheveigné, Lucas...
113
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ACL
2010
14 years 7 months ago
Starting from Scratch in Semantic Role Labeling
A fundamental step in sentence comprehension involves assigning semantic roles to sentence constituents. To accomplish this, the listener must parse the sentence, find constituent...
Michael Connor, Yael Gertner, Cynthia Fisher, Dan ...
BMCBI
2010
155views more  BMCBI 2010»
14 years 9 months ago
A flexible R package for nonnegative matrix factorization
Background: Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face re...
Renaud Gaujoux, Cathal Seoighe
MM
2006
ACM
203views Multimedia» more  MM 2006»
15 years 3 months ago
Learning image manifolds by semantic subspace projection
In many image retrieval applications, the mapping between highlevel semantic concept and low-level features is obtained through a learning process. Traditional approaches often as...
Jie Yu, Qi Tian
DAGM
2010
Springer
14 years 10 months ago
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...
Hannes Nickisch, Carl Edward Rasmussen