Unsupervised dimensionality reduction: the challenge of big data visulisation
Year: 2015
Type: book-chapter
Abstract: Dimensionality reduction is an unsupervised task that allows high-dimensional data to be processed or visualised in lower-dimensional spaces. This tutorial reviews the basic principles of dimensionality reduction and discusses some of the approaches that were published over the past years from the perspective of their application to big data. The tutorial ends with a short review of papers about dimensionality reduction in these proceedings, as well as some perspectives for the near future.
Source: Digital Access to Libraries
Authors: Kerstin Bunte, John A. Lee
Institutions: Aalto University, UCLouvain
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2464721407