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» A Distance-Based Packing Method for High Dimensional Data
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ICDE
2012
IEEE
208views Database» more  ICDE 2012»
11 years 11 months ago
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data
—Traditional clustering algorithms identify just a single clustering of the data. Today’s complex data, however, allow multiple interpretations leading to several valid groupin...
Emmanuel Müller, Stephan Günnemann, Ines...
PRL
2011
13 years 3 months ago
Efficient approximate Regularized Least Squares by Toeplitz matrix
Machine Learning based on the Regularized Least Square (RLS) model requires one to solve a system of linear equations. Direct-solution methods exhibit predictable complexity and s...
Sergio Decherchi, Paolo Gastaldo, Rodolfo Zunino
ICCS
2007
Springer
14 years 2 months ago
Hessian-Based Model Reduction for Large-Scale Data Assimilation Problems
Assimilation of spatially- and temporally-distributed state observations into simulations of dynamical systems stemming from discretized PDEs leads to inverse problems with high-di...
Omar Bashir, Omar Ghattas, Judith Hill, Bart G. va...
ICCS
2005
Springer
14 years 2 months ago
Dimension Reduction for Clustering Time Series Using Global Characteristics
Existing methods for time series clustering rely on the actual data values can become impractical since the methods do not easily handle dataset with high dimensionality, missing v...
Xiaozhe Wang, Kate A. Smith, Rob J. Hyndman
MMM
2011
Springer
251views Multimedia» more  MMM 2011»
13 years 14 days ago
Randomly Projected KD-Trees with Distance Metric Learning for Image Retrieval
Abstract. Efficient nearest neighbor (NN) search techniques for highdimensional data are crucial to content-based image retrieval (CBIR). Traditional data structures (e.g., kd-tree...
Pengcheng Wu, Steven C. H. Hoi, Duc Dung Nguyen, Y...