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PAMI
2008
162views more  PAMI 2008»
14 years 9 months ago
Dimensionality Reduction of Clustered Data Sets
We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution...
Guido Sanguinetti
JMLR
2010
110views more  JMLR 2010»
14 years 8 months ago
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
Nonlinear dimensionality reduction methods are often used to visualize high-dimensional data, although the existing methods have been designed for other related tasks such as mani...
Jarkko Venna, Jaakko Peltonen, Kristian Nybo, Hele...
CSDA
2007
58views more  CSDA 2007»
14 years 9 months ago
A unifying model involving a categorical and/or dimensional reduction for multimode data
A unifying model is presented that implies a categorical and/or dimensional reduction of one or several modes of a multiway data set. The model encompasses a broad range of (exist...
Iven Van Mechelen, Jan Schepers
SIGMOD
2002
ACM
246views Database» more  SIGMOD 2002»
15 years 9 months ago
Hierarchical subspace sampling: a unified framework for high dimensional data reduction, selectivity estimation and nearest neig
With the increased abilities for automated data collection made possible by modern technology, the typical sizes of data collections have continued to grow in recent years. In suc...
Charu C. Aggarwal
PAMI
2006
141views more  PAMI 2006»
14 years 9 months ago
Diffusion Maps and Coarse-Graining: A Unified Framework for Dimensionality Reduction, Graph Partitioning, and Data Set Parameter
We provide evidence that non-linear dimensionality reduction, clustering and data set parameterization can be solved within one and the same framework. The main idea is to define ...
Stéphane Lafon, Ann B. Lee