@inproceedings{c737837ba33447d398426980db23a437,
title = "Subspace mapping of noisy text documents",
abstract = "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.",
keywords = "Compressed Document Representation, Subspace Mapping",
author = "Soto, {Axel J.} and Marc Strickert and Vazquez, {Gustavo E.} and Evangelos Milios",
year = "2011",
doi = "10.1007/978-3-642-21043-3_45",
language = "Ingl{\'e}s",
isbn = "9783642210426",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "377--383",
booktitle = "Advances in Artificial Intelligence - 24th Canadian Conference on Artificial Intelligence, Canadian AI 2011, Proceedings",
}