Progression‐Preserving Dimension Reduction for High‐Dimensional Sensor Data Visualization

Dimension Reduction Algorithms for Data Mining and Visualization

Scalable Dimension Reduction for Large Abstract Data Visualization

The ability to browse vast amounts of scientific data is critical to facilitate science discovery. High performance Multidimensional Scaling (MDS) algorithm makes it a reality by reducing dimensions so that scientists can gain insight into data set from a 3D visualization space. As multidimensional scaling requires quadratics order of physical memory and computation, a major challenge is to des...

Nonlinear Dimension Reduction and Visualization of Labeled Data

D imension reduction and visualization of high-dimensional data constitutes one of the key problems of data mining[1, 2]. Since embedding in lower dimensions necessarily includes a loss of information, methods to explicitly control the information kept by a specific dimension reduction technique are highly desirable. The incorporation of supervised class information constitutes an important spe...

Alignment-free Visualization of Metagenomic Data by Nonlinear Dimension Reduction

The visualization of metagenomic data, especially without prior taxonomic identification of reconstructed genomic fragments, is a challenging problem in computational biology. An ideal visualization method should, among others, enable clear distinction of congruent groups of sequences of closely related taxa, be applicable to fragments of lengths typically achievable following assembly, and all...

Dimension Reduction and Data Visualization Using Neural Networks

The problem of visual presentation of multidimensional data is discussed. The projection methods for dimension reduction are reviewed. The chapter deals with the artificial neural networks that may be used for reducing dimension and data visualization, too. The stress is put on combining the selforganizing map (SOM) and Sammon mapping and on the neural network for Sammon’s mapping SAMANN. Large...