Tensor-Train Networks for Learning Predictive Modeling of Multidimensional Data Article Swipe
Michele Nazareth da Costa
,
Romis Attux
,
Andrzej Cichocki
,
João Marcos Travassos Romano
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2101.09184
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2101.09184
In this work, we firstly apply the Train-Tensor (TT) networks to construct a compact representation of the classical Multilayer Perceptron, representing a reduction of up to 95% of the coefficients. A comparative analysis between tensor model and standard multilayer neural networks is also carried out in the context of prediction of the Mackey-Glass noisy chaotic time series and NASDAQ index. We show that the weights of a multidimensional regression model can be learned by means of TT network and the optimization of TT weights is a more robust to the impact of coefficient initialization and hyper-parameter setting. Furthermore, an efficient algorithm based on alternating least squares has been proposed for approximating the weights in TT-format with a reduction of computational calculus, providing a much faster convergence than the well-known adaptive learning-method algorithms, widely applied for optimizing neural networks.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2101.09184
- https://arxiv.org/pdf/2101.09184
- OA Status
- green
- Cited By
- 2
- References
- 64
- Related Works
- 10
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- https://openalex.org/W3121472434
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https://openalex.org/W3121472434Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.2101.09184Digital Object Identifier
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Tensor-Train Networks for Learning Predictive Modeling of Multidimensional DataWork title
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preprintOpenAlex work type
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enPrimary language
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2021Year of publication
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2021-01-22Full publication date if available
- Authors
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Michele Nazareth da Costa, Romis Attux, Andrzej Cichocki, João Marcos Travassos RomanoList of authors in order
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https://arxiv.org/abs/2101.09184Publisher landing page
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https://arxiv.org/pdf/2101.09184Direct link to full text PDF
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greenOpen access status per OpenAlex
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Tensor (intrinsic definition), Computer science, Artificial intelligence, Machine learning, Mathematics, GeometryTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2021: 2Per-year citation counts (last 5 years)
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64Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.calculus, | 120 |
| abstract_inverted_index.classical | 17 |
| abstract_inverted_index.construct | 11 |
| abstract_inverted_index.efficient | 99 |
| abstract_inverted_index.networks. | 137 |
| abstract_inverted_index.providing | 121 |
| abstract_inverted_index.reduction | 22, 117 |
| abstract_inverted_index.Multilayer | 18 |
| abstract_inverted_index.multilayer | 38 |
| abstract_inverted_index.optimizing | 135 |
| abstract_inverted_index.prediction | 49 |
| abstract_inverted_index.regression | 68 |
| abstract_inverted_index.well-known | 128 |
| abstract_inverted_index.Perceptron, | 19 |
| abstract_inverted_index.algorithms, | 131 |
| abstract_inverted_index.alternating | 103 |
| abstract_inverted_index.coefficient | 92 |
| abstract_inverted_index.comparative | 31 |
| abstract_inverted_index.convergence | 125 |
| abstract_inverted_index.Furthermore, | 97 |
| abstract_inverted_index.Mackey-Glass | 52 |
| abstract_inverted_index.Train-Tensor | 7 |
| abstract_inverted_index.optimization | 80 |
| abstract_inverted_index.representing | 20 |
| abstract_inverted_index.approximating | 110 |
| abstract_inverted_index.coefficients. | 29 |
| abstract_inverted_index.computational | 119 |
| abstract_inverted_index.initialization | 93 |
| abstract_inverted_index.representation | 14 |
| abstract_inverted_index.hyper-parameter | 95 |
| abstract_inverted_index.learning-method | 130 |
| abstract_inverted_index.multidimensional | 67 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |