Abstract
Dimensionality reduction is a hot research topic in data analysis today. Thanks to the advances in high performance computing technologies and in the engineering field, we entered in the so-called big-data era and an enormous quantity of data is available in every scientific area, ranging from social networking, economy and politics to e-health and life sciences. However, much of the data is highly redundant and can be efficiently brought down to a much smaller number of variables without a significant loss of information using different strategies.
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