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» Dimensionality Reduction of Clustered Data Sets
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CIBCB
2006
IEEE
14 years 2 months ago
Visualization of Support Vector Machines with Unsupervised Learning
– The visualization of support vector machines in realistic settings is a difficult problem due to the high dimensionality of the typical datasets involved. However, such visuali...
Lutz Hamel
IJCM
2007
205views more  IJCM 2007»
13 years 8 months ago
BTF modelling using BRDF texels
The highest fidelity representations of realistic real-world materials currently used comprise Bidirectional Texture Functions (BTF). The BTF is a six dimensional function dependi...
Jirí Filip, Michal Haindl
SDM
2003
SIAM
120views Data Mining» more  SDM 2003»
13 years 10 months ago
Estimation of Topological Dimension
We present two extensions of the algorithm by Broomhead et al [2] which is based on the idea that singular values that scale linearly with the radius of the data ball can be explo...
Douglas R. Hundley, Michael J. Kirby
JMLR
2012
11 years 11 months ago
Maximum Margin Temporal Clustering
Temporal Clustering (TC) refers to the factorization of multiple time series into a set of non-overlapping segments that belong to k temporal clusters. Existing methods based on e...
Minh Hoai Nguyen, Fernando De la Torre
TSP
2008
151views more  TSP 2008»
13 years 8 months ago
Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors
The rapid developing area of compressed sensing suggests that a sparse vector lying in a high dimensional space can be accurately and efficiently recovered from only a small set of...
Moshe Mishali, Yonina C. Eldar