Unsupervised Feature Selection with Controlled Redundancy (UFeSCoR)
Abstract— Unsupervised Feature Selection with Controlled Redundancy (UFeSCoR). Features selected by a supervised / unsupervised technique often include redundant or correlated features. While use of correlated features may result in an increase in the design and decision making cost, removing redundancy completely can make the system vulnerable to measurement errors. Most feature selection schemes do not account for redundancy at all, while a few supervised methods try to discard correlated features. We propose a novel unsupervised feature selection scheme UFeSCoR, < Final Year Projects 2016 > which not only discards irrelevant features, but also selects features with controlled redundancy. Here the number of selected features can also be directed. Our algorithm optimizes an objective function, which tries to select a specified number of features.
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