Members
-
Hiroshi Sawada (Group Leader) Kazuo Aoyama Jun Imai Tomoharu Iwata Katsuhiko Ishiguro Tatsushi Matsubayashi Tokuaki Kawamae Daichi Mochihashi (Research Associate) Hitoshi 'keen' Sakano Kenichi Arai (Innovacative Communication Labotratory) -
Affiliates
-
Naonori Ueda (Deputy Director) Takeshi Yamada (Science and Core Technology Laboratory Group) Shuhei Kuwata (NTT DATA) Kazumi Saito (University of Shizuoka) - @
Research Overview
Discover and visualize latent structures in relational data
In our modern society, there is a huge amount of information available, and people have to deal with more information than they are able to process to make sensible decisions. We are pursuing statistical machine learning approaches to establish highly sophisticated data-mining technologies to analyze, organize, classify and visualize information. By understanding generative and evolution processes of the information, we can discover important but latent causes for observable interactions and relations in the data, and make predictions. In short, our aim is to make full use of information as knowledge. In particular, our research topics include gvisualization of large-scale relational datah that enables us to visualize latent structures and characteristics of relational data, gfast similarity search for large-scale datah by organizing data as a network, "modeling purchase behavior" and "multiple object tracking and clustering" both of which extract and model aspects of human behaviors.
Future Directions
All the social phenomena occur as results of accumulated interactions between humans or human and information in various levels. We believe that we can unravel key mechanism of the generative processes of those social phenomena through the analysis and understanding of those interactions. For example, by analyzing the information flows and interactions on the whole Internet, we may able to predict future growth, anticipate the trend and early sign of anomaly, and visualize whole Internet structure. This is like a weather forecast system for the Internet. Furthermore, by analyzing all possible sensory data that monitor every aspect of human activities, we may able to categorize human daily and social activities into patterns and predict future actions that enable us to avoid potential risks, find others who have similar activity patterns, be advised next possible actions and improve technical skills or expertise. In short, we will be able to build a total navigation system of human daily life.Visualizing large-scale relational data
We study visualizing large-scale networks with hundreds of thousands of nodes fast and accurately, based on a force-directed model and inspired by a hierarchical approach known in the astronomy.
We also study simultaneous extracting and visualizing both documents and semantic "topics" of the documents.
Fast similarity search for large-scale data
We study efficient similarity search methods for large-scale data that utilizes a network with a small-world property as an indexing structure.
Extracting patterns from human behaviors
We try to model human behavior of purchasing items to discover innovative users who purchase items earlier than others and to improve customer lifetime value by recommending items that will help extending subscription period. We also study simultaneous tracking objects such as humans and extracting the number of objects, positions and dynamics patterns in a movie at the same time.
