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Spectral Learning on Matrices and Tensors (Paperback)
Loot Price: R2,217
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Spectral Learning on Matrices and Tensors (Paperback)
Series: Foundations and Trends (R) in Machine Learning
Expected to ship within 10 - 15 working days
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The authors of this monograph survey recent progress in using
spectral methods including matrix and tensor decomposition
techniques to learn many popular latent variable models. With
careful implementation, tensor-based methods can run efficiently in
practice, and in many cases they are the only algorithms with
provable guarantees on running time and sample complexity. The
focus is on a special type of tensor decomposition called CP
decomposition, and the authors cover a wide range of algorithms to
find the components of such tensor decomposition. They also discuss
the usefulness of this decomposition by reviewing several
probabilistic models that can be learned using such tensor methods.
The second half of the monograph looks at practical applications.
This includes using Tensorly, an efficient tensor algebra software
package, which has a simple python interface for expressing tensor
operations. It also has a flexible back-end system supporting
NumPy, PyTorch, TensorFlow, and MXNet. Spectral Learning on
Matrices and Tensors provides a theoretical and practical
introduction to designing and deploying spectral learning on both
matrices and tensors. It is of interest for all students,
researchers and practitioners working on modern day machine
learning problems.
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