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.