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JMLR
2006
136views more  JMLR 2006»
15 years 3 months ago
Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
In this paper we consider a novel Bayesian interpretation of Fisher's discriminant analysis. We relate Rayleigh's coefficient to a noise model that minimises a cost base...
Tonatiuh Peña Centeno, Neil D. Lawrence
PRL
2010
209views more  PRL 2010»
14 years 10 months ago
Efficient update of the covariance matrix inverse in iterated linear discriminant analysis
For fast classification under real-time constraints, as required in many imagebased pattern recognition applications, linear discriminant functions are a good choice. Linear discr...
Jan Salmen, Marc Schlipsing, Christian Igel
128
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ICMLA
2008
15 years 4 months ago
An Improved Generalized Discriminant Analysis for Large-Scale Data Set
In order to overcome the computation and storage problem for large-scale data set, an efficient iterative method of Generalized Discriminant Analysis is proposed. Because sample v...
Weiya Shi, Yue-Fei Guo, Cheng Jin, Xiangyang Xue
ICDM
2009
IEEE
174views Data Mining» more  ICDM 2009»
15 years 10 months ago
Non-sparse Multiple Kernel Learning for Fisher Discriminant Analysis
—We consider the problem of learning a linear combination of pre-specified kernel matrices in the Fisher discriminant analysis setting. Existing methods for such a task impose a...
Fei Yan, Josef Kittler, Krystian Mikolajczyk, Muha...
SPEECH
1998
171views more  SPEECH 1998»
15 years 2 months ago
Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition
We present the theory for heteroscedastic discriminant analysis (HDA), a model-based generalization of linear discriminant analysis (LDA) derived in the maximum-likelihood framewo...
Nagendra Kumar, Andreas G. Andreou