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» Dimensionality reduced HRTFs: a comparative study
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ACMACE
2008
ACM
10 years 6 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...
NIPS
2001
10 years 5 months ago
Estimating Car Insurance Premia: a Case Study in High-Dimensional Data Inference
Estimating insurance premia from data is a difficult regression problem for several reasons: the large number of variables, many of which are discrete, and the very peculiar shape...
Nicolas Chapados, Yoshua Bengio, Pascal Vincent, J...
ICDE
2003
IEEE
193views Database» more  ICDE 2003»
11 years 5 months ago
An Adaptive and Efficient Dimensionality Reduction Algorithm for High-Dimensional Indexing
The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well known approach to o...
Hui Jin, Beng Chin Ooi, Heng Tao Shen, Cui Yu, Aoy...
ICPR
2006
IEEE
10 years 10 months ago
Class Separability in Spaces Reduced By Feature Selection
We investigated the geometrical complexity of several high-dimensional, small sample classiļ¬cation problems and its changes due to two popular feature selection procedures, forw...
Erinija Pranckeviciene, TinKam Ho, Ray L. Somorjai
VLDB
2007
ACM
174views Database» more  VLDB 2007»
11 years 4 months ago
An adaptive and dynamic dimensionality reduction method for high-dimensional indexing
Abstract The notorious "dimensionality curse" is a wellknown phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approa...
Heng Tao Shen, Xiaofang Zhou, Aoying Zhou
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