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VLSISP
2010
254views more  VLSISP 2010»
15 years 5 months ago
Manifold Based Local Classifiers: Linear and Nonlinear Approaches
Abstract In case of insufficient data samples in highdimensional classification problems, sparse scatters of samples tend to have many ‘holes’—regions that have few or no nea...
Hakan Cevikalp, Diane Larlus, Marian Neamtu, Bill ...
TIP
2010
182views more  TIP 2010»
15 years 1 months ago
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the ...
Feiping Nie, Dong Xu, Ivor Wai-Hung Tsang, Changsh...
KDD
2004
ACM
123views Data Mining» more  KDD 2004»
16 years 7 months ago
A DEA approach for model combination
This paper proposes a novel Data Envelopment Analysis (DEA) based approach for model combination. We first prove that for the 2-class classification problems DEA models identify t...
Zhiqiang Zheng, Balaji Padmanabhan, Haoqiang Zheng
SADFE
2009
IEEE
16 years 1 months ago
File Fragment Classification-The Case for Specialized Approaches
Increasingly advances in file carving, memory analysis and network forensics requires the ability to identify the underlying type of a file given only a file fragment. Work to dat...
Vassil Roussev, Simson L. Garfinkel
ACCV
2009
Springer
16 years 1 months ago
Lorentzian Discriminant Projection and Its Applications
This paper develops a supervised dimensionality reduction method, Lorentzian Discriminant Projection (LDP), for discriminant analysis and classification. Our method represents the...
Risheng Liu, Zhixun Su, Zhouchen Lin, Xiaoyu Hou