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Article 14, Pub. date: October 2010
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Multilabel Dimensionality Reduction via Dependence Maximization YIN ZHANG and ZHI-HUA ZHOU Nanjing University, China Multilabel learning deals with data associated with multiple labels simultaneously. Like other data mining and machine learning tasks, multilabel learning also suffers from the curse of dimensionality. Dimensionality reduction has been studied for many years, however, multilabel dimensionality reduction remains almost untouched. In this article, we propose a multilabel dimensionality reduction method, MDDM, with two kinds of projection strategies, attempting to project the original data into a lower-dimensional feature space maximizing the dependence between the original feature description and the associated class labels. Based on the Hilbert-Schmidt Independence Criterion, we derive a eigen-decomposition problem which enables the dimensionality reduction process to be ef cient. Experiments validate the performance of MDDM. Categories and Subject Descriptors: H.2.8 [Database Management]: Data Mining; I.2.6 [Arti cial Intelligence]: Learning General Terms: Algorithms, Design, Experimentation Additional Key Words and Phrases: Dimensionality reduction, multilabel learning ACM Reference Format: Zhang, Y. and Zhou, Z.-H. 2010. Multilabel dimensionality reduction via dependence maximization. ACM Trans. Knowl. Discov. Data. 4, 3, Article 14 (October 2010), 21 pages. DOI = 10.1145/1839490.1839495 http://doi.acm.org/10.1145/1839490.1839495 1. INTRODUCTION In traditional supervised learning, each instance is associated with one label that
ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery
Published: Oct 1, 2010
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