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170views
13 years 7 months ago
Covariance Regularization for Supervised Learning in High Dimensions
This paper studies the effect of covariance regularization for classific ation of high-dimensional data. This is done by fitting a mixture of Gaussians with a regularized covaria...
Daniel L. Elliott, Charles W. Anderson, Michael Ki...
SDM
2012
SIAM
261views Data Mining» more  SDM 2012»
11 years 11 months ago
Combining Active Learning and Dynamic Dimensionality Reduction
To date, many active learning techniques have been developed for acquiring labels when training data is limited. However, an important aspect of the problem has often been neglect...
Mustafa Bilgic
ICDE
2003
IEEE
193views Database» more  ICDE 2003»
14 years 10 months ago
An Adaptive and Efficient Dimensionality Reduction Algorithm for High-Dimensional Indexing
The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well known approach to o...
Hui Jin, Beng Chin Ooi, Heng Tao Shen, Cui Yu, Aoy...
CALC
2001
Springer
145views Cryptology» more  CALC 2001»
14 years 1 months ago
Fast Reduction of Ternary Quadratic Forms
We show that a positive definite integral ternary form can be reduced with O(M(s) log2 s) bit operations, where s is the binary encoding length of the form and M(s) is the bit-com...
Friedrich Eisenbrand, Günter Rote
SYRCODIS
2007
126views Database» more  SYRCODIS 2007»
13 years 9 months ago
Concept Lattice Reduction by Singular Value Decomposition
High complexity of lattice construction algorithms and uneasy way of visualising lattices are two important problems connected with the formal concept analysis. Algorithm complexi...
Václav Snásel, Martin Polovincak, Hu...