Pages that link to "Item:Q1887709"
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The following pages link to An efficient algorithm to solve the small sample size problem for LDA (Q1887709):
Displaying 14 items.
- A novel kernel-based maximum a posteriori classification method (Q280412) (← links)
- Feature extraction using two-dimensional local graph embedding based on maximum margin criterion (Q555361) (← links)
- Feature extraction using constrained maximum variance mapping (Q941563) (← links)
- Locally linear discriminant embedding: An efficient method for face recognition (Q948015) (← links)
- An efficient discriminant-based solution for small sample size problem (Q1010095) (← links)
- A generalised K-L expansion method which can deal with small sample size and high-dimensional problems (Q1400851) (← links)
- MBLDA: a novel multiple between-class linear discriminant analysis (Q2282060) (← links)
- Why direct LDA is not equivalent to LDA (Q2369640) (← links)
- A study on three linear discriminant analysis based methods in small sample size problem (Q2384958) (← links)
- A comparison of generalized linear discriminant analysis algorithms (Q2462602) (← links)
- Generalized null space uncorrelated Fisher discriminant analysis for linear dimensionality reduction (Q2498671) (← links)
- A note on kernel uncorrelated discriminant analysis (Q2568087) (← links)
- Class-Incremental Generalized Discriminant Analysis (Q5468703) (← links)
- A direct LDA algorithm for high-dimensional data -- with application to face recognition (Q5948555) (← links)
