Author: Hull, Isaiah
Title: Dimensionality Reduction Cord-id: ffj7nlhh Document date: 2020_11_26
ID: ffj7nlhh
Snippet: Many problem classes in machine learning are inherently high dimensional. Natural language processing problems, for instance, often involve the extraction of meaning from words, which can appear in an intractably large number of potential sequences in writing. In this chapter, we will discuss how principal component analysis (PCA), partial least squares (PLS), and autoencoder models can be used to reduce the dimensionality of such problems, rendering them tractable.
Document: Many problem classes in machine learning are inherently high dimensional. Natural language processing problems, for instance, often involve the extraction of meaning from words, which can appear in an intractably large number of potential sequences in writing. In this chapter, we will discuss how principal component analysis (PCA), partial least squares (PLS), and autoencoder models can be used to reduce the dimensionality of such problems, rendering them tractable.
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