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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Learning Multi-Way Relations via Tensor Decomposition With Neural Networks

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Koji Maruhashi

Fujitsu Laboratories Ltd.


Masaru Todoriki

Fujitsu Laboratories Ltd.


Takuya Ohwa

Fujitsu Laboratories Ltd.


Keisuke Goto

Fujitsu Laboratories Ltd.


Yu Hasegawa

Fujitsu Laboratories Ltd.


Hiroya Inakoshi

Fujitsu Laboratories Ltd.


Hirokazu Anai

Fujitsu Laboratories Ltd.


DOI:

10.1609/aaai.v32i1.11760


Abstract:

How can we classify multi-way data such as network traffic logs with multi-way relations between source IPs, destination IPs, and ports? Multi-way data can be represented as a tensor, and there have been several studies on classification of tensors to date. One critical issue in the classification of multi-way relations is how to extract important features for classification when objects in different multi-way data, i.e., in different tensors, are not necessarily in correspondence. In such situations, we aim to extract features that do not depend on how we allocate indices to an object such as a specific source IP; we are interested in only the structures of the multi-way relations. However, this issue has not been considered in previous studies on classification of multi-way data. We propose a novel method which can learn and classify multi-way data using neural networks. Our method leverages a novel type of tensor decomposition that utilizes a target core tensor expressing the important features whose indices are independent of those of the multi-way data. The target core tensor guides the tensor decomposition into more effective results and is optimized in a supervised manner. Our experiments on three different domains show that our method is highly accurate, especially on higher order data. It also enables us to interpret the classification results along with the matrices calculated with the novel tensor decomposition.

Topics: AAAI

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HOW TO CITE:

Koji Maruhashi||Masaru Todoriki||Takuya Ohwa||Keisuke Goto||Yu Hasegawa||Hiroya Inakoshi||Hirokazu Anai Learning Multi-Way Relations via Tensor Decomposition With Neural Networks Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Koji Maruhashi||Masaru Todoriki||Takuya Ohwa||Keisuke Goto||Yu Hasegawa||Hiroya Inakoshi||Hirokazu Anai Learning Multi-Way Relations via Tensor Decomposition With Neural Networks AAAI 2018, .

Koji Maruhashi||Masaru Todoriki||Takuya Ohwa||Keisuke Goto||Yu Hasegawa||Hiroya Inakoshi||Hirokazu Anai (2018). Learning Multi-Way Relations via Tensor Decomposition With Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Koji Maruhashi||Masaru Todoriki||Takuya Ohwa||Keisuke Goto||Yu Hasegawa||Hiroya Inakoshi||Hirokazu Anai. Learning Multi-Way Relations via Tensor Decomposition With Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Koji Maruhashi||Masaru Todoriki||Takuya Ohwa||Keisuke Goto||Yu Hasegawa||Hiroya Inakoshi||Hirokazu Anai. 2018. Learning Multi-Way Relations via Tensor Decomposition With Neural Networks. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Koji Maruhashi||Masaru Todoriki||Takuya Ohwa||Keisuke Goto||Yu Hasegawa||Hiroya Inakoshi||Hirokazu Anai. (2018) "Learning Multi-Way Relations via Tensor Decomposition With Neural Networks", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Koji Maruhashi||Masaru Todoriki||Takuya Ohwa||Keisuke Goto||Yu Hasegawa||Hiroya Inakoshi||Hirokazu Anai, "Learning Multi-Way Relations via Tensor Decomposition With Neural Networks", AAAI, p., 2018.

Koji Maruhashi||Masaru Todoriki||Takuya Ohwa||Keisuke Goto||Yu Hasegawa||Hiroya Inakoshi||Hirokazu Anai. "Learning Multi-Way Relations via Tensor Decomposition With Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Koji Maruhashi||Masaru Todoriki||Takuya Ohwa||Keisuke Goto||Yu Hasegawa||Hiroya Inakoshi||Hirokazu Anai. "Learning Multi-Way Relations via Tensor Decomposition With Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Koji Maruhashi||Masaru Todoriki||Takuya Ohwa||Keisuke Goto||Yu Hasegawa||Hiroya Inakoshi||Hirokazu Anai. Learning Multi-Way Relations via Tensor Decomposition With Neural Networks. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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