A method to explain the correlation relationships from data with many variables using a few fewer variables!
Principal component analysis is one of the statistical analysis methods. It is a technique that explains the correlation relationships of data with many variables using a smaller number of variables (principal components). By synthesizing or compressing data with many variables into a few principal components while minimizing information loss, one can succinctly understand the overall picture of the data. *For detailed content of the glossary, please refer to the related links. For more information, feel free to contact us.
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*For detailed information about the glossary, you can view it through the related links. For more information, please 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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