December 20, 2019 Friday Lunch Seminar
12:15 〜 13:00
CiNet 1F Conference Room
“Multiple co-clustering and its application to identify subtypes of depressive disorder”
Neural Information Analysis Laboratories, ATR
Host PI: Okito Yamashita
Cluster analysis is a powerful data mining tool to reveal the underlying heterogeneous structure of objects in data. Recently, a co-clustering methods gains much attention for its attempt to reveal relationships between object and feature, hence capturing a possible interplay between them. However, in a big dataset, multiple cluster structures may exist, where cluster solutions differ depending on the features that one focusses on. To cope with this challenge, we developed a novel multiple co-clustering method. Our method is based on non-parametric Bayesian mixture models in which features are optimally partitioned for each cluster solution. This feature partition works as feature selection for a particular cluster solution, screening out irrelevant features. In this talk, I present a theoretical foundation of the method, and show how it works to identify subtypes of depressive disorder using high-dimensional data of different modalities such as functional Magnetic Resonance Imaging (fMRI) and clinical questionnaire scores (Tokuda et al., SciRep, 2018).
About CiNet’s Friday Lunch Seminars:
The Friday Lunch Seminar is CiNet’s main regular meeting series, held every week at 12:15 in the beautiful main lecture theatre on the ground floor at CiNet. The talks are typically 40mins long and orientated towards an inter-disciplinary audience. They are informal, social, and most people bring their own lunch to eat during the talk. They are open to anyone who is feeling curious and wants to come, regardless of where you work.