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» Dimensionality reduction and generalization
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SDM
2004
SIAM
162views Data Mining» more  SDM 2004»
13 years 11 months ago
Subspace Clustering of High Dimensional Data
Clustering suffers from the curse of dimensionality, and similarity functions that use all input features with equal relevance may not be effective. We introduce an algorithm that...
Carlotta Domeniconi, Dimitris Papadopoulos, Dimitr...
ICCD
2008
IEEE
117views Hardware» more  ICCD 2008»
14 years 6 months ago
Two dimensional highly associative level-two cache design
High associativity is important for level-two cache designs [9]. Implementing CAM-based Highly Associative Caches (CAM-HAC), however, is both costly in hardware and exhibits poor s...
Chuanjun Zhang, Bing Xue
ICPR
2006
IEEE
14 years 11 months ago
Non-Iterative Two-Dimensional Linear Discriminant Analysis
Linear discriminant analysis (LDA) is a well-known scheme for feature extraction and dimensionality reduction of labeled data in a vector space. Recently, LDA has been extended to...
Kohei Inoue, Kiichi Urahama
BMCBI
2010
243views more  BMCBI 2010»
13 years 10 months ago
Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data
Background: Visualization of DNA microarray data in two or three dimensional spaces is an important exploratory analysis step in order to detect quality issues or to generate new ...
Christoph Bartenhagen, Hans-Ulrich Klein, Christia...
ICML
2008
IEEE
14 years 10 months ago
Dirichlet component analysis: feature extraction for compositional data
We consider feature extraction (dimensionality reduction) for compositional data, where the data vectors are constrained to be positive and constant-sum. In real-world problems, t...
Hua-Yan Wang, Qiang Yang, Hong Qin, Hongbin Zha