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» Learning Multiple Latent Variables with Self-Organizing Maps
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ICCV
2007
IEEE
14 years 9 months ago
The Joint Manifold Model for Semi-supervised Multi-valued Regression
Many computer vision tasks may be expressed as the problem of learning a mapping between image space and a parameter space. For example, in human body pose estimation, recent rese...
Ramanan Navaratnam, Andrew W. Fitzgibbon, Roberto ...
SSPR
2004
Springer
14 years 1 months ago
Finding Clusters and Components by Unsupervised Learning
We present a tutorial survey on some recent approaches to unsupervised machine learning in the context of statistical pattern recognition. In statistical PR, there are two classica...
Erkki Oja
ICASSP
2011
IEEE
12 years 11 months ago
A partial least squares framework for speaker recognition
Modern approaches to speaker recognition (verification) operate in a space of “supervectors” created via concatenation of the mean vectors of a Gaussian mixture model (GMM) a...
Balaji Vasan Srinivasan, Dmitry N. Zotkin, Ramani ...
CIKM
2009
Springer
14 years 11 days ago
Heterogeneous cross domain ranking in latent space
Traditional ranking mainly focuses on one type of data source, and effective modeling still relies on a sufficiently large number of labeled or supervised examples. However, in m...
Bo Wang, Jie Tang, Wei Fan, Songcan Chen, Zi Yang,...
TRECVID
2007
13 years 9 months ago
PicSOM Experiments in TRECVID 2007
Our experiments in TRECVID 2007 include participation in the high-level feature extraction, search, and video summarization tasks, using a common system framework based on multipl...
Markus Koskela, Mats Sjöberg, Ville Viitaniem...