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Tensor Networks Dimensionality Reduction by Cichocki Andrzej (17 results)
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Tensor Networks for Dimensionality Reduction and Large-scale Optimization
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
US$ 120.33
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Add to basketPAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Tensor Networks for Dimensionality Reduction and Large-scale Optimization : Part 1 Low-rank Tensor Decompositions
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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US$ 120.32
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Tensor Networks for Dimensionality Reduction and Large-Scale Optimization
Published by Now Publishers Dez 2016, 2016
ISBN 10: 1680832220 ISBN 13: 9781680832228
Language: English
US$ 111.83
Convert currencyUS$ 30.62 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketTaschenbuch. Condition: Neu. Neuware -Modern applications in engineering and data science are increasingly based on multidimensional data of exceedingly high volume, variety, and structural richness. However, standard machine learning and data mining algorithms typically scale exponentially with data volume and complexity of cross-modal couplings - the so called curse of dimensionality - which is prohibitive to the analysis of such large-scale, multi-modal and multi-relational datasets. Given that such data are often conveniently represented as multiway arrays or tensors, it is therefore timely and valuable for the multidisciplinary machine learning and data analytic communities to review tensor decompositions and tensor networks as emerging tools for dimensionality reduction and large scale optimization.This monograph provides a systematic and example-rich guide to the basic properties and applications of tensor network methodologies, and demonstrates their promise as a tool for the analysis of extreme-scale multidimensional data. It demonstrates the ability of tensor networks to provide linearly or even super-linearly, scalable solutions.The low-rank tensor network framework of analysis presented in this monograph is intended to both help demystify tensor decompositions for educational purposes and further empower practitioners with enhanced intuition and freedom in algorithmic design for the manifold applications. In addition, the material may be useful in lecture courses on large-scale machine learning and big data analytics, or indeed, as interesting reading for the intellectually curious and generally knowledgeable reader.
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Tensor Networks for Dimensionality Reduction and Large-scale Optimization : Part 1 Low-rank Tensor Decompositions
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 137.12
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Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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US$ 152.40
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Add to basketPaperback / softback. Condition: New. New copy - Usually dispatched within 4 working days. 283.
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Condition: New. pp. 196.
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Condition: New. pp. 262.
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US$ 149.71
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Add to basketPaperback. Condition: Brand New. 196 pages. 9.21x6.14x0.42 inches. In Stock.
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Tensor Networks for Dimensionality Reduction and Large-Scale Optimization : Part 1 Low-Rank Tensor Decompositions
Published by Now Publishers Dez 2016, 2016
ISBN 10: 1680832220 ISBN 13: 9781680832228
Language: English
US$ 142.31
Convert currencyUS$ 72.62 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketTaschenbuch. Condition: Neu. Neuware - Modern applications in engineering and data science are increasingly based on multidimensional data of exceedingly high volume, variety, and structural richness. However, standard machine learning and data mining algorithms typically scale exponentially with data volume and complexity of cross-modal couplings - the so called curse of dimensionality - which is prohibitive to the analysis of such large-scale, multi-modal and multi-relational datasets. Given that such data are often conveniently represented as multiway arrays or tensors, it is therefore timely and valuable for the multidisciplinary machine learning and data analytic communities to review tensor decompositions and tensor networks as emerging tools for dimensionality reduction and large scale optimization.This monograph provides a systematic and example-rich guide to the basic properties and applications of tensor network methodologies, and demonstrates their promise as a tool for the analysis of extreme-scale multidimensional data. It demonstrates the ability of tensor networks to provide linearly or even super-linearly, scalable solutions.The low-rank tensor network framework of analysis presented in this monograph is intended to both help demystify tensor decompositions for educational purposes and further empower practitioners with enhanced intuition and freedom in algorithmic design for the manifold applications. In addition, the material may be useful in lecture courses on large-scale machine learning and big data analytics, or indeed, as interesting reading for the intellectually curious and generally knowledgeable reader.
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. KlappentextModern applications in engineering and data science are increasingly based on multidimensional data of exceedingly high volume, variety, and structural richness. However, standard machine learning and data mining algorithms ty.
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Add to basketCondition: New. Print on Demand pp. 196.
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Add to basketCondition: New. Print on Demand pp. 262.
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Add to basketCondition: New. PRINT ON DEMAND pp. 196.
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Add to basketCondition: New. PRINT ON DEMAND pp. 262.
