Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that a...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...
Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a dataset. This is a particularly important challenge with...
Linear subspace learning (LSL) is a popular approach to image recognition and it aims to reveal the essential features of high dimensional data, e.g., facial images, in a lower di...
Background: A method to evaluate and analyze the massive data generated by series of microarray experiments is of utmost importance to reveal the hidden patterns of gene expressio...
Junbai Wang, Jan Delabie, Hans Christian Aasheim, ...
Abstract— We propose the Multi-resolution Correlation Cluster detection (MrCC), a novel, scalable method to detect correlation clusters able to analyze dimensional data in the ra...
Robson Leonardo Ferreira Cordeiro, Agma J. M. Trai...