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IJCNN
2006
IEEE
14 years 1 months ago
Nonlinear principal component analysis of noisy data
With very noisy data, having plentiful samples eliminates overfitting in nonlinear regression, but not in nonlinear principal component analysis (NLPCA). To overcome this problem...
William W. Hsieh
ICIP
2002
IEEE
14 years 9 months ago
Hybrid and parallel face classifier based on artificial neural networks and principal component analysis
We present a hybrid and parallel system based on artificial neural networks for a face invariant classifier and general pattern recognition problems. A set of face features is ext...
Peter V. Bazanov, Tae-Kyun Kim, Seok-Cheol Kee, Sa...
PKDD
2010
Springer
160views Data Mining» more  PKDD 2010»
13 years 6 months ago
Sparse Unsupervised Dimensionality Reduction Algorithms
Abstract. Principal component analysis (PCA) and its dual—principal coordinate analysis (PCO)—are widely applied to unsupervised dimensionality reduction. In this paper, we sho...
Wenjun Dou, Guang Dai, Congfu Xu, Zhihua Zhang
ISNN
2005
Springer
14 years 1 months ago
Neural Network Based Online Feature Selection for Vehicle Tracking
Abstract. Aiming at vehicle tracking with a single moving camera for autonomous driving, this paper presents a strategy of online feature selection combined with related process fr...
Tie Liu, Nanning Zheng, Hong Cheng
BMCBI
2005
118views more  BMCBI 2005»
13 years 7 months ago
Feature selection and nearest centroid classification for protein mass spectrometry
Background: The use of mass spectrometry as a proteomics tool is poised to revolutionize early disease diagnosis and biomarker identification. Unfortunately, before standard super...
Ilya Levner