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» Stahel-Donoho estimation for high-dimensional data
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ICML
2006
IEEE
16 years 7 months ago
Bayesian regression with input noise for high dimensional data
This paper examines high dimensional regression with noise-contaminated input and output data. Goals of such learning problems include optimal prediction with noiseless query poin...
Jo-Anne Ting, Aaron D'Souza, Stefan Schaal
ICDT
2001
ACM
116views Database» more  ICDT 2001»
15 years 10 months ago
On Optimizing Nearest Neighbor Queries in High-Dimensional Data Spaces
Abstract. Nearest-neighbor queries in high-dimensional space are of high importance in various applications, especially in content-based indexing of multimedia data. For an optimiz...
Stefan Berchtold, Christian Böhm, Daniel A. K...
PCM
2001
Springer
183views Multimedia» more  PCM 2001»
15 years 10 months ago
An Adaptive Index Structure for High-Dimensional Similarity Search
A practical method for creating a high dimensional index structure that adapts to the data distribution and scales well with the database size, is presented. Typical media descrip...
Peng Wu, B. S. Manjunath, Shivkumar Chandrasekaran
TSP
2008
117views more  TSP 2008»
15 years 6 months ago
Sample Eigenvalue Based Detection of High-Dimensional Signals in White Noise Using Relatively Few Samples
The detection and estimation of signals in noisy, limited data is a problem of interest to many scientific and engineering communities. We present a mathematically justifiable, com...
R. R. Nadakuditi, A. Edelman
ICPR
2006
IEEE
16 years 7 months ago
Exploiting High Dimensional Video Features Using Layered Gaussian Mixture Models
Analysis of video data usually requires training classifiers in high dimensional feature spaces. This paper proposes a layered Gaussian mixture model (LGMM) to exploit high dimens...
Datong Chen, Jie Yang