This updated compendium provides the linear algebra background
necessary to understand and develop linear algebra applications in
data mining and machine learning.Basic knowledge and advanced new
topics (spectral theory, singular values, decomposition techniques
for matrices, tensors and multidimensional arrays) are presented
together with several applications of linear algebra (k-means
clustering, biplots, least square approximations, dimensionality
reduction techniques, tensors and multidimensional arrays).The
useful reference text includes more than 600 exercises and
supplements, many with completed solutions and MATLAB
applications.The volume benefits professionals, academics,
researchers and graduate students in the fields of pattern
recognition/image analysis, AI, machine learning and databases.
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