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» Optimizing for parallelism and data locality
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85
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ICDM
2002
IEEE
191views Data Mining» more  ICDM 2002»
15 years 3 months ago
Iterative Clustering of High Dimensional Text Data Augmented by Local Search
The k-means algorithm with cosine similarity, also known as the spherical k-means algorithm, is a popular method for clustering document collections. However, spherical k-means ca...
Inderjit S. Dhillon, Yuqiang Guan, J. Kogan
76
Voted
ICASSP
2007
IEEE
15 years 4 months ago
Local Linear Discriminant Analysis (LLDA) for Inference of Multisubject FMRI Data
Large intersubject variability is a well-described feature of fMRI studies, making inter-group inference, of critical importance for biological interpretation, difficult. Therefor...
Martin J. McKeown, Junning Li, Xuemei Huang, Z. Ja...
109
Voted
EUROPAR
2000
Springer
15 years 1 months ago
Ahnentafel Indexing into Morton-Ordered Arrays, or Matrix Locality for Free
Abstract. Definitions for the uniform representation of d-dimensional matrices serially in Morton-order (or Z-order) support both their use with cartesian indices, and their divide...
David S. Wise
DCOSS
2010
Springer
15 years 1 months ago
Approximate Distributed Kalman Filtering for Cooperative Multi-agent Localization
Abstract. We consider the problem of estimating the locations of mobile agents by fusing the measurements of displacements of the agents as well as relative position measurements b...
Prabir Barooah, Wm. Joshua Russell, João P....
TIP
2008
113views more  TIP 2008»
14 years 10 months ago
Phase Local Approximation (PhaseLa) Technique for Phase Unwrap From Noisy Data
The local polynomial approximation (LPA) is a nonparametric regression technique with pointwise estimation in a sliding window. We apply the LPA of the argument of cos and sin in o...
Vladimir Katkovnik, Jaakko Astola, Karen O. Egiaza...