Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Two-dimensional imaging
Dimension reduction by local principal component analysis
Neural Computation
A Hierarchical Latent Variable Model for Data Visualization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixtures of probabilistic principal component analyzers
Neural Computation
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent component analysis: algorithms and applications
Neural Networks
Fractional-Step Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
The Journal of Machine Learning Research
Data mapping by probabilistic modular networks andinformation-theoretic criteria
IEEE Transactions on Signal Processing
Modeling the manifolds of images of handwritten digits
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A high-performance VLSI architecture for the histogram peak-climbing data clustering algorithm
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Journal of Biomedical Informatics
Incremental non-gaussian analysis of microarray gene expression data
Proceedings of the third international workshop on Data and text mining in bioinformatics
Hi-index | 0.00 |
Recent advances in machine learning and pattern recognition methods provide new analytical tools to explore high dimensional gene expression microarray data. Our data mining software, VISual Data Analyzer for cluster discovery (VISDA), reveals many distinguishing patterns among gene expression profiles, which are responsible for the cell's phenotypes. The model-supported exploration of high-dimensional data space is achieved through two complementary schemes: dimensionality reduction by discriminatory data projection and cluster decomposition by soft data clustering. Reducing dimensionality generates the visualization of the complete data set at the top level. This data set is then partitioned into subclusters that can consequently be visualized at lower levels and if necessary partitioned again. In this paper, three different algorithms are evaluated in their abilities to reduce dimensionality and to visualize data sets: Principal Component Analysis (PCA), Discriminatory Component Analysis (DCA), and Projection Pursuit Method (PPM). The partitioning into subclusters uses the Expectation-Maximization (EM) algorithm and the hierarchical normal mixture model that is selected by the user and verified "optimally" by the Minimum Description Length (MDL) criterion. These approaches produce different visualizations that are compared against known phenotypes from the microarray experiments. Overall, these algorithms and user-selected models explore the high dimensional data where standard analyses may not be sufficient.