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» Supervised dimensionality reduction using mixture models
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ICML
2007
IEEE
16 years 16 days ago
Map building without localization by dimensionality reduction techniques
This paper proposes a new map building framework for mobile robot named Localization-Free Mapping by Dimensionality Reduction (LFMDR). In this framework, the robot map building is...
Takehisa Yairi
ACCV
2009
Springer
15 years 6 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
SDM
2011
SIAM
233views Data Mining» more  SDM 2011»
14 years 2 months ago
Multi-Instance Mixture Models
Multi-instance (MI) learning is a variant of supervised learning where labeled examples consist of bags (i.e. multi-sets) of feature vectors instead of just a single feature vecto...
James R. Foulds, Padhraic Smyth
ICML
2009
IEEE
16 years 16 days ago
Partially supervised feature selection with regularized linear models
This paper addresses feature selection techniques for classification of high dimensional data, such as those produced by microarray experiments. Some prior knowledge may be availa...
Thibault Helleputte, Pierre Dupont
BMCBI
2010
122views more  BMCBI 2010»
14 years 12 months ago
Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data
Background: Recent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduc...
Kai-Lin Tang, Tong-Hua Li, Wen-Wei Xiong, Kai Chen