Abstract
Efficient processing and interpretation of high-dimensional data sets, prevalent in a large number of problems related to engineering and management, has become an essential requirement that has to be addressed in the d:sign and analysis of modem manufacturing systems. After a review of dimensionality reduction methods, this paper proposes the integration of reduced-dimensionality representations with sparse data filtering algorithms. The spatial filtering procedures implement an attention focusing mechanism that guides the user in locating the objects or data set segments most relevant to the user task. A non-linear dimensionality reduction method, capable of dealing with highly non-linear data distribution patterns, is investigated in more detail. Examples in visual inspection and robot navigation control are provided.
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