Unsupervised Dimensionality Reduction Algorithm

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An Unsupervised Dimensionality Reduction Algorithm is a dimensionality reduction algorithm (to solve a dimensionality reduction task) that is a unsupervised learning algorithm.




  • (Koller & Sahami, 1996) ⇒ Daphne Koller and Mehran Sahami. (1996). “Toward Optimal Feature Selection.” In: Proceedings of the International Conference on Machine Learning (ICML 1996).
    • ABSTRACT: In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for defining the theoretically optimal, but computationally intractable, method for feature subset selection is presented. We show that our goal should be to eliminate a feature if it gives us little or no additional information beyond that subsumed by the remaining features. In particular, this will be the case for both irrelevant and redundant features. We then give an efficient algorithm for feature selection which computes an approximation to the optimal feature selection criterion. The conditions under which the approximate algorithm is successful are examined. Empirical results are given on a number of data sets, showing that the algorithm effectively handles datasets with large numbers of features.