Subspace mapping of noisy text documents

Axel J. Soto, Marc Strickert, Gustavo E. Vazquez, Evangelos Milios

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

Subspace mapping methods aim at projecting high-dimensional data into a subspace where a specific objective function is optimized. Such dimension reduction allows the removal of collinear and irrelevant variables for creating informative visualizations and task-related data spaces. These specific and generally de-noised subspaces spaces enable machine learning methods to work more efficiently. We present a new and general subspace mapping method, Correlative Matrix Mapping (CMM), and evaluate its abilities for category-driven text organization by assessing neighborhood preservation, class coherence, and classification. This approach is evaluated for the challenging task of processing short and noisy documents.

Idioma originalInglés
Título de la publicación alojadaAdvances in Artificial Intelligence - 24th Canadian Conference on Artificial Intelligence, Canadian AI 2011, Proceedings
EditorialSpringer Verlag
Páginas377-383
Número de páginas7
ISBN (versión impresa)9783642210426
DOI
EstadoPublicada - 2011
Publicado de forma externa

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen6657 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Huella

Profundice en los temas de investigación de 'Subspace mapping of noisy text documents'. En conjunto forman una huella única.

Citar esto