Understand the potential structure of data and clarify the underlying factors behind consumer behavior!
Factor analysis is a multivariate analytical method used to identify common factors hidden within multivariate data. This method is based on the idea that unobservable latent factors (common factors) influence multiple observed variables. By identifying factors common to many observed variables, factor analysis helps to understand the underlying structure of the data and clarifies the factors behind consumer behavior and other phenomena. *For detailed content of the glossary, please refer to the related links. For more information, feel free to contact us.*
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Our company was established on May 25, 2018, through joint investment by Keio Corporation and Professor Maki Sakamoto of the National University Corporation, University of Electro-Communications. We are a company that can commercially utilize the intellectual property from the Sakamoto Laboratory at the University of Electro-Communications, which has been certified as a venture originating from the university. By creating AI that understands the hidden senses within people and supports their expression, we aim to become a platform for the utilization of sensitivity.
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