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CORR
2008
Springer
91views Education» more  CORR 2008»
14 years 9 months ago
Particle Filtering for Large Dimensional State Spaces with Multimodal Observation Likelihoods
We study efficient importance sampling techniques for particle filtering (PF) when either (a) the observation likelihood (OL) is frequently multimodal or heavy-tailed, or (b) the s...
Namrata Vaswani
PAKDD
2009
ACM
186views Data Mining» more  PAKDD 2009»
15 years 4 months ago
Pairwise Constrained Clustering for Sparse and High Dimensional Feature Spaces
Abstract. Clustering high dimensional data with sparse features is challenging because pairwise distances between data items are not informative in high dimensional space. To addre...
Su Yan, Hai Wang, Dongwon Lee, C. Lee Giles
HAIS
2009
Springer
15 years 2 months ago
Unsupervised Feature Selection in High Dimensional Spaces and Uncertainty
Developing models and methods to manage data vagueness is a current effervescent research field. Some work has been done with supervised problems but unsupervised problems and unce...
José Ramón Villar, María del ...
CVPR
1997
IEEE
15 years 11 months ago
Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces
Shape indexing is a way of making rapid associations between features detected in an image and object models that could have produced them. When model databases are large, the use...
Jeffrey S. Beis, David G. Lowe
WSC
2001
14 years 11 months ago
Dimensionality analysis of a simulation outcome space
This paper investigates the dimensionality characteristics of the outcome space of a combat simulation. The independent state variables of all of the outcome states for a simulati...
John B. Gilmer Jr., Frederick J. Sullivan