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This book provides a concise but comprehensive guide to
representation, which forms the core of Machine Learning (ML).
State-of-the-art practical applications involve a number of
challenges for the analysis of high-dimensional data.
Unfortunately, many popular ML algorithms fail to perform, in both
theory and practice, when they are confronted with the huge size of
the underlying data. Solutions to this problem are aptly covered in
the book. In addition, the book covers a wide range of
representation techniques that are important for academics and ML
practitioners alike, such as Locality Sensitive Hashing (LSH),
Distance Metrics and Fractional Norms, Principal Components (PCs),
Random Projections and Autoencoders. Several experimental results
are provided in the book to demonstrate the discussed techniques'
effectiveness.
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