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ICDM
2003
IEEE
153views Data Mining» more  ICDM 2003»
15 years 11 months ago
Dimensionality Reduction Using Kernel Pooled Local Discriminant Information
We study the use of kernel subspace methods for learning low-dimensional representations for classification. We propose a kernel pooled local discriminant subspace method and com...
Peng Zhang, Jing Peng, Carlotta Domeniconi
BIOWIRE
2007
Springer
16 years 1 days ago
Beta Random Projection
Random projection (RP) is a common technique for dimensionality reduction under L2 norm for which many significant space embedding results have been demonstrated. In particular, r...
Yu-En Lu, Pietro Liò, Steven Hand
ICPR
2008
IEEE
16 years 9 days ago
Clustering-based locally linear embedding
The locally linear embedding (LLE) algorithm is considered as a powerful method for the problem of nonlinear dimensionality reduction. In this paper, first, a new method called cl...
Kanghua Hui, Chunheng Wang
ACMACE
2008
ACM
15 years 8 months ago
Dimensionality reduced HRTFs: a comparative study
Dimensionality reduction is a statistical tool commonly used to map high-dimensional data into lower a dimensionality. The transformed data is typically more suitable for regressi...
Bill Kapralos, Nathan Mekuz, Agnieszka Kopinska, S...
ICASSP
2011
IEEE
14 years 9 months ago
Linear manifold approximation based on differences of tangents
In this paper, we consider the problem of manifold approximation with affine subspaces. Our objective is to discover a set of low dimensional affine subspaces that represents ma...
Sofia Karygianni, Pascal Frossard