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The main objective of this chapter is to explain the two important dimensionality reduction techniques, feature hashing and principal component analysis, that can support scaling-up machine learning. The standard and flagged feature hashing approaches are explained in detail. The feature hashing approach suffers from the hash collision problem, and this problem is reported and discussed in detail in this chapter, too. Two collision controllers, feature binning and feature mitigation, are also proposed in this chapter to address this problem. The principal component analysis uses the concepts of eigenvalues and eigenvectors, and these terminologies are explained in detail with examples. The principal component analysis is also explained using a simple two-dimensional example, and several coding examples are also presented.
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http://www.math.northwestern.edu/~mlerma/papers/princcomp2d.pdf, Last accessed: May 14, 2015.
- Dimensionality Reduction
- Springer US
- Chapter 14
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