I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large col...
Background: In discriminant analysis of microarray data, usually a small number of samples are expressed by a large number of genes. It is not only difficult but also unnecessary ...
In this paper, we present a novel algorithm for incremental principal component analysis. Based on the LargestEigenvalue-Theory, i.e. the eigenvector associated with the largest ei...
This paper presents an algorithm for multiscale image segmentation. Towards this, it proposes a new region model, that of a homogenous region surrounded by ramp discontinuities (a...
The kernel Perceptron is an appealing online learning algorithm that has a drawback: whenever it makes an error it must increase its support set, which slows training and testing ...