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» Large-scale manifold learning
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ML
2010
ACM
193views Machine Learning» more  ML 2010»
13 years 4 months ago
On the eigenvectors of p-Laplacian
Spectral analysis approaches have been actively studied in machine learning and data mining areas, due to their generality, efficiency, and rich theoretical foundations. As a natur...
Dijun Luo, Heng Huang, Chris H. Q. Ding, Feiping N...
NIPS
2007
13 years 11 months ago
People Tracking with the Laplacian Eigenmaps Latent Variable Model
Reliably recovering 3D human pose from monocular video requires models that bias the estimates towards typical human poses and motions. We construct priors for people tracking usi...
Zhengdong Lu, Miguel Á. Carreira-Perpi&ntil...
DAGM
2010
Springer
13 years 11 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
ICADL
2005
Springer
137views Education» more  ICADL 2005»
14 years 3 months ago
A Collaborative Filtering Based Re-ranking Strategy for Search in Digital Libraries
Users of a digital book library system typically interact with the system to search for books by querying on the metadata describing the books or to search for information in the p...
U. Rohini, Vamshi Ambati
BMCBI
2010
193views more  BMCBI 2010»
13 years 4 months ago
Mayday - integrative analytics for expression data
Background: DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the ge...
Florian Battke, Stephan Symons, Kay Nieselt