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Chapter
Statistical Learning Approaches
Instead of retaining certain properties when selecting or extracting features, other methods aim to remove irrelevant and/or redundant features in the data using primarily statistical criteria. Features are no...
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Chapter
Metaheuristics of DR Methods
This chapter synthesizes key heuristics distilled from a number of methods that can be applied to dimensionality reduction, leveraging choices such as feature grou** and domain knowledge, as well as the meta...
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Chapter
Conventional Statistical Approaches
The objective of dimensionality reduction is to retain key properties of the given data to solve a problem with fewer features in a lower dimensional space. Statistical methods aim to preserve characteristic p...
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Chapter
Appendices
This chapter presents a summary review of prerequisite concepts from statistics, mathematics and computer science, although readers are expected to have a nodding familiarity with most of them. It also provide...
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Book
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Chapter
What Is Data Science (DS)?
Our ability to generate, gather, and store volumes of data (order of tera- and exo-bytes (1012–1018 bytes) daily) has far outpaced our ability to derive useful information from it in many fields, with available c...
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Article
An Information-theoretic approach to dimensionality reduction in data science
Data reduction is crucial in order to turn large datasets into information, the major purpose of data science. The classic and richer area of dimensionality reduction (DR) has traditionally been based on featu...
