- Knowledge discovery based on probabilistic latent variable models -
Tomoharu Iwata, Ueda Research Laboratory
With the rapid growth of Internet and sensors, we can easily obtain and accumulate a huge amount of data. Automatic discovery of useful knowledge from data, therefore, becomes an important challenge in big data analysis. In this talk, I am going to explain a generative model approach that can automatically find intrinsic latent features from the given data. Then, I will provide guidelines for modeling data by introducing specific models for some applications, such as topic extraction and object matching.