Learning Multi-Way Relations via Tensor Decomposition With Neural Networks
By Koji Maruhashi, Masaru Todoriki, Takuya Ohwa, Keisuke Goto, Yu Hasegawa, Hiroya Inakoshi, Hirokazu Anai. 2018
27 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computational Mathematics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Andreas Holzinger
- Shuiwang Ji
- Haiyang Yu
- Hao Yuan
- Katharina Holzinger
- Randy Goebel
- Shurui Gui
- Peter Kieseberg
In Computational Mathematics
Citations in the corpus
- Explainable AI: The New 42? (2018, background): “Deep Tensor [16] is a deep neural network that is especially suited to datasets with meaningful graph-like properties.”
- Explainability in Graph Neural Networks: A Taxonomic Survey (2020, background): “These facts motivate us to develop deep learning methods for real-world applications [6], [7], [8], [9] in interdisciplinary domains, such as finance, biology, and agriculture etc.”