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» Lossy Reduction for Very High Dimensional Data
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CSC
2006
15 years 1 months ago
An Adaptive Method for Flow Simulation in Three-Dimensional Heterogeneous Discrete Fracture Networks
Natural fractured media are highly unpredictable because of existing complex structures at the fracture and at the network levels. Fractures are by themselves heterogeneous objects...
Hussein Mustapha
AAAI
2008
15 years 2 months ago
Adapting ADtrees for High Arity Features
ADtrees, a data structure useful for caching sufficient statistics, have been successfully adapted to grow lazily when memory is limited and to update sequentially with an increme...
Robert Van Dam, Irene Langkilde-Geary, Dan Ventura
SDM
2011
SIAM
370views Data Mining» more  SDM 2011»
14 years 2 months ago
Sparse Latent Semantic Analysis
Latent semantic analysis (LSA), as one of the most popular unsupervised dimension reduction tools, has a wide range of applications in text mining and information retrieval. The k...
Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin, Jaime G....
ICMCS
2005
IEEE
79views Multimedia» more  ICMCS 2005»
15 years 5 months ago
Supervised semi-definite embedding for image manifolds
Semi-definite Embedding (SDE) has been a recently proposed to maximize the sum of pair wise squared distances between outputs while the input data and outputs are locally isometri...
Benyu Zhang, Jun Yan, Ning Liu, QianSheng Cheng, Z...
ICANN
2003
Springer
15 years 5 months ago
Supervised Locally Linear Embedding
Locally linear embedding (LLE) is a recently proposed method for unsupervised nonlinear dimensionality reduction. It has a number of attractive features: it does not require an ite...
Dick de Ridder, Olga Kouropteva, Oleg Okun, Matti ...