Principal component analysis has proven to be useful for understanding geometric variability in populations of parameterized objects. The statistical framework is well understood ...
The small sample size problem is often encountered in pattern recognition. It results in the singularity of the within-class scatter matrix Sw in Linear Discriminant Analysis (LDA...
We present a novel approach to embedding data represented by a network into a lowdimensional Euclidean space. Unlike existing methods, the proposed method attempts to minimize an ...
—This paper presents a system-level Network-on-Chip modeling framework that integrates transaction-level model and analytical wire model for design space exploration. It enables ...
This paper presents a novel discriminative learning method, called Manifold Discriminant Analysis (MDA), to solve the problem of image set classification. By modeling each image s...