The existence of numeric data and large amounts of records in a database pose a challenging task to explicit concepts extraction from the raw data. This paper introduces a method that reduces data vertically and horizontally, keeps the discriminating power of the original data, and paves the way for extracting concepts. The method is based on discretization (vertical reduction) and feature sele...

The nervous system extracts information from its environment and distributes and processes that information to inform and drive behaviour. In this task, the nervous system faces a type of data analysis problem, for, while a visual scene may be overflowing with information, reaching for the television remote before us requires extraction of only a relatively small fraction of that information. W...

Abstract To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences. Because sample sizes are typically orders magnitude smaller than dimensionality these data, valid inferences require finding a low-dimensional representation p...

Plant has plenty use in foodstuff, medicine and industry, and is also vitally important for environmental protection. So, it is important and urgent to recognize and classify plant species. Plant classification based on leaf images is a basic research of botanical area and agricultural production. Due to the high nature complexity and high dimensionality of leaf image data, dimensional reductio...

Nonlinear dimensionality reduction (DR) techniques offer the possibility to visually inspect a high-dimensional data set in two dimensions, and such methods have recently been extended to also visualize class boundaries as induced by a trained classifier on the data. In this contribution, we investigate the effect of two different ways to shape the involved dimensionality reduction technique in...

Indexing issues that arise in the support of similarity searching are presented. This includes a discussion of the curse of dimensionality, as well as multidimensional indexing, distance-based indexing, dimension reduction, and embedding methods.