Proposal of dimention reduction method based on waveform similarity and synthetic wave principle
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- HIRUTA Komei
- Keio University
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- TAKAYA Eichi
- Keio University
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- KURIHARA Satoshi
- Keio University
Bibliographic Information
- Other Title
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- 波形類似度と合成波の原理に基づく多次元系列データ次元削減法の提案
Abstract
<p>To promote social implementation of Society 5.0, it is essential to detect various kinds of information in the real world. With the complexity of the real world, the obtained data inevitably become hyper-multi-dimensional. In this study, we propose a new method of dimensionality reduction that effectively exploits the latent wave properties of many time series data. Specifically, the first step is to cluster the multidimensional time series data into a specific number of clusters based on similarity. Then, assuming that data belonging to the same cluster exist in the same wavelength band, the synthetic wave principle is applied. Based on the physical fact that waves after superimposition of waves of different wavelengths can be represented by the harmonic mean of each wave, a dimensionality reduction is performed that preserves the information in the original multidimensional data. in this way, we propose dimensionality reduction method that can compress each variable with less information loss than conventional methods.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2023 (0), 2A5GS203-2A5GS203, 2023
The Japanese Society for Artificial Intelligence
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Keywords
Details 詳細情報について
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- CRID
- 1390578283197838208
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- ISSN
- 27587347
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- Text Lang
- ja
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- Data Source
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- JaLC
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- Abstract License Flag
- Disallowed