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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
122
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PODS
2001
ACM
190views Database» more  PODS 2001»
15 years 9 months ago
On the Effects of Dimensionality Reduction on High Dimensional Similarity Search
The dimensionality curse has profound e ects on the effectiveness of high-dimensional similarity indexing from the performance perspective. One of the well known techniques for im...
Charu C. Aggarwal
VLSISP
2002
139views more  VLSISP 2002»
14 years 9 months ago
A Modified Minimum Classification Error (MCE) Training Algorithm for Dimensionality Reduction
Dimensionality reduction is an important problem in pattern recognition. There is a tendency of using more and more features to improve the performance of classifiers. However, not...
Xuechuan Wang, Kuldip K. Paliwal
AUSDM
2007
Springer
107views Data Mining» more  AUSDM 2007»
15 years 3 months ago
A Discriminant Analysis for Undersampled Data
One of the inherent problems in pattern recognition is the undersampled data problem, also known as the curse of dimensionality reduction. In this paper a new algorithm called pai...
Matthew Robards, Junbin Gao, Philip Charlton
JMLR
2010
115views more  JMLR 2010»
14 years 4 months ago
O-IPCAC and its Application to EEG Classification
In this paper we describe an online/incremental linear binary classifier based on an interesting approach to estimate the Fisher subspace. The proposed method allows to deal with ...
Alessandro Rozza, Gabriele Lombardi, Marco Rosa, E...