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PR
2008
161views more  PR 2008»
15 years 6 months ago
A study on three linear discriminant analysis based methods in small sample size problem
In this paper, we make a study on three Linear Discriminant Analysis (LDA) based methods: Regularized Discriminant Analysis (RDA), Discriminant Common Vectors (DCV) and Maximal Ma...
Jun Liu, Songcan Chen, Xiaoyang Tan
ITC
2003
IEEE
183views Hardware» more  ITC 2003»
15 years 12 months ago
Future Challenges for MEMS Failure Analysis
MEMS processes and components are rapidly changing in device design, processing, and, most importantly, application. This paper will discuss the future challenges faced by the MEM...
Jeremy A. Walraven
BMCBI
2010
146views more  BMCBI 2010»
15 years 6 months ago
Nonnegative principal component analysis for mass spectral serum profiles and biomarker discovery
Background: As a novel cancer diagnostic paradigm, mass spectroscopic serum proteomic pattern diagnostics was reported superior to the conventional serologic cancer biomarkers. Ho...
Henry Han
NECO
1998
151views more  NECO 1998»
15 years 6 months ago
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can e ciently compute principal comp...
Bernhard Schölkopf, Alex J. Smola, Klaus-Robe...
KDD
2006
ACM
115views Data Mining» more  KDD 2006»
16 years 7 months ago
Supervised probabilistic principal component analysis
Principal component analysis (PCA) has been extensively applied in data mining, pattern recognition and information retrieval for unsupervised dimensionality reduction. When label...
Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Krieg...