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JMLR
2006
104views more  JMLR 2006»
11 years 1 months ago
Learning Image Components for Object Recognition
In order to perform object recognition it is necessary to learn representations of the underlying components of images. Such components correspond to objects, object-parts, or fea...
Michael W. Spratling
IJON
2008
116views more  IJON 2008»
11 years 1 months ago
Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint
Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can ...
Paul D. O'Grady, Barak A. Pearlmutter
ACL
2004
11 years 2 months ago
Aligning words using matrix factorisation
Aligning words from sentences which are mutual translations is an important problem in different settings, such as bilingual terminology extraction, Machine Translation, or projec...
Cyril Goutte, Kenji Yamada, Éric Gaussier
NIPS
2008
11 years 2 months ago
Bayesian Exponential Family PCA
Principal Components Analysis (PCA) has become established as one of the key tools for dimensionality reduction when dealing with real valued data. Approaches such as exponential ...
Shakir Mohamed, Katherine A. Heller, Zoubin Ghahra...
EWCBR
2008
Springer
11 years 3 months ago
An Analysis of Research Themes in the CBR Conference Literature
After fifteen years of CBR conferences, this paper sets out to examine the themes that have evolved in CBR research as revealed by the implicit and explicit relationships between t...
Derek Greene, Jill Freyne, Barry Smyth, Padraig Cu...
CIVR
2008
Springer
166views Image Analysis» more  CIVR 2008»
11 years 3 months ago
Non-negative matrix factorisation for object class discovery and image auto-annotation
In information retrieval, sub-space techniques are usually used to reveal the latent semantic structure of a data-set by projecting it to a low dimensional space. Non-negative mat...
Jiayu Tang, Paul H. Lewis
SLSFS
2005
Springer
11 years 6 months ago
Discrete Component Analysis
Abstract. This article presents a uniļ¬ed theory for analysis of components in discrete data, and compares the methods with techniques such as independent component analysis, non-...
Wray L. Buntine, Aleks Jakulin
ICA
2007
Springer
11 years 7 months ago
Discovering Convolutive Speech Phones Using Sparseness and Non-negativity
Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can b...
Paul D. O'Grady, Barak A. Pearlmutter
ICASSP
2008
IEEE
11 years 7 months ago
Unsupervised learning of auditory filter banks using non-negative matrix factorisation
Non-negative matrix factorisation (NMF) is an unsupervised learning technique that decomposes a non-negative data matrix into a product of two lower rank non-negative matrices. Th...
Alexander Bertrand, Kris Demuynck, Veronique Stout...
ICASSP
2008
IEEE
11 years 7 months ago
Bayesian extensions to non-negative matrix factorisation for audio signal modelling
We describe the underlying probabilistic generative signal model of non-negative matrix factorisation (NMF) and propose a realistic conjugate priors on the matrices to be estimate...
Tuomas Virtanen, Ali Taylan Cemgil, Simon J. Godsi...
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