Knowledge discovery from high-dimensional data based on combinatorial computation
Finished by March 31, 2014
Osaka University, The Institute of Scientific and Industrial Research, Associate Professor
Against the backdrop of accelerating progress of data acquisition technologies, there are more scenes where we deal with high-dimensional data in a variety of engineering problems, such as bioinformatics, natural language processing and image data processing. The purpose of this research is to build a data-mining framework for global analysis of high-dimensional data based on combinatorial computation, using the discrete data structure called submodularity. And, we aim at discovering important knowledge in a variety of applications by applying the developed algorithms to real-world data.