IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Advanced Data Mining and Visualization Techniques with Probabilistic Principal Surfaces: Applications to Astronomy and Genetics

Advanced Data Mining and Visualization Techniques with Probabilistic Principal Surfaces: Applications to Astronomy and Genetics
View Sample PDF
Author(s): Antonino Staiano (University of Napoli, “Parthenope”, Italy), Lara De Vinco (Nexera S.c.p.A., Italy), Giuseppe Longo (University of Salerno, Italy)and Roberto Tagliaferri (University of Napoli, “Parthenope”, Italy)
Copyright: 2008
Pages: 21
Source title: Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59904-951-9.ch123

Purchase


Abstract

Probabilistic Principal Surfaces (PPS) is a non linear latent variable model with very powerful visualization and classification capabilities which seem to be able to overcome most of the shortcomings of other neural tools. PPS builds a probability density function of a given set of patterns lying in a high-dimensional space which can be expressed in terms of a fixed number of latent variables lying in a latent Q-dimensional space. Usually, the Q-space is either two or three dimensional and thus the density function can be used to visualize the data within it. The case in which Q = 3 allows to project the patterns on a spherical manifold which turns out to be optimal when dealing with sparse data. PPS may also be arranged in ensembles to tackle complex classification tasks. As template cases we discuss the application of PPS to two real- world data sets from astronomy and genetics.

Related Content

Nuno Silva, Pedro Sousa, Miguel Mira da Silva. © 2019. 19 pages.
Ioannis Routis, Mara Nikolaidou, Nancy Alexopoulou. © 2019. 21 pages.
Jeffrey S. Zanzig, Guillermo A. Francia III, Xavier P. Francia. © 2019. 26 pages.
S. B. Goyal. © 2019. 30 pages.
Maria João Ferreira, Fernando Moreira, Isabel Seruca. © 2019. 24 pages.
Agostino Poggi, Paolo Fornacciari, Gianfranco Lombardo, Monica Mordonini, Michele Tomaiuolo. © 2019. 21 pages.
Rüdiger Pryss, Manfred Reichert. © 2019. 26 pages.
Body Bottom