Big Data Science

Infinite data analysis beyond big data

- Stochastic process models for infinite-dimensional matrices -

Abstract

We address the problem of infinite data analysis. Big data analysis has been a very active area of research in machine learning, but so far, as implied by the term ‘big data’ itself, the amount of input data has been assumed to be finite. However, many types of data often grow infinitely in size, and therefore, the observed data must be a part of a potentially infinite amount of data. This is the reason why we think the machine learning systems must be able to handle unlimited size of data. As an example of our results, this presentation deals with relational data represented by matrices, where the rows indicate instances and the columns represent values attributed to the instances.

Photos

Poster


Please click the thumbnail image to open the full-size PDF file.

Presenters

Masahiro Nakano
Masahiro Nakano
Media Information Laboratory
Katsuhiko Ishiguro
Katsuhiko Ishiguro
Innovative Communication Laboratory
Akisato Kimura
Akisato Kimura
Media Information Laboratory
Takeshi Yamada
Takeshi Yamada
Research Planning section
Naonori Ueda
Naonori Ueda
Machine Learning and Data Science Center