The dimensionality curse has greatly aaected the scalability of high-dimensional indexes. A well known approach to improving the indexing performance is dimensionality reduction before indexing the data in the reduced-dimensionality space. However, the reduction may cause loss of distance information when the data set is not globally correlated. To reduce loss of information and degradation of ...

This paper investigates a possibility of supplementing standard dimensionality reduction procedures, used in the process of knowledge extraction from multidimensional datasets, with topology preservation measures. This approach is based on an observation that not all elements of an initial dataset are equally preserved in its low-dimensional embedding space representation. The contribution firs...

Dimension Reduction for Model-Based Clustering via Mixtures of Multivariate t-Distributions Katherine Morris Advisor University of Guelph, 2012 Prof. Paul D. McNicholas We introduce a dimension reduction method for model-based clustering obtained from a finite mixture of t-distributions. This approach is based on existing work on reducing dimensionality in the case of finite Gaussian mixtures. ...

DNA micro-array technologies has made it possible to monitor the expression of thousands of genes across a set of conditions or cases simultaneously, enabling us to study the functional or regulatory relationship among genes. Assuming that genes with similar expression profiles have similar functions or are located in the same regulatory pathway, various methods of gene clustering and dimension...