Spectral Learning on Matrices and Tensors
Seiten
2019
now publishers Inc (Verlag)
9781680836400 (ISBN)
now publishers Inc (Verlag)
9781680836400 (ISBN)
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Surveys recent progress in using spectral methods, including matrix and tensor decomposition techniques, to learn many popular latent variable models. The focus is on a special type of tensor decomposition called CP decomposition. The authors cover a wide range of algorithms to find the components of such tensor decomposition.
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.
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.
1. Introduction
2. Matrix Decomposition
3. Tensor Decomposition Algorithms
4. Applications of Tensor Methods
5. Practical Implementations
6. Efficiency of Tensor Decomposition
7. Overcomplete Tensor Decomposition
Acknowledgements
References
| Erscheinungsdatum | 30.11.2019 |
|---|---|
| Reihe/Serie | Foundations and Trends® in Machine Learning |
| Verlagsort | Hanover |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 230 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| ISBN-13 | 9781680836400 / 9781680836400 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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