[2A5-GS-2] Machine learning

[2A5-GS-2-03] Proposal of dimention reduction method based on waveform similarity and synthetic wave principle

〇Komei Hiruta¹, Eichi Takaya¹, Satoshi Kurihara¹ (1. Keio University)

Keywords:Data Mining, Dimension Reduction, Synthetic Wave

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.