Philip S. Yu
Broad Learning on Big Data via Fusion of Heterogeneous Information
Abstract
In the era of big data, there are abundant of data available across many different data sources in various formats. “Broad Learning” is a
new type of learning task, which focuses on fusing multiple large-scale information sources of diverse varieties together and carrying out
synergistic data mining tasks across these fused sources in one unified analytic. Great challenges exist on “Broad Learning” for the effective
fusion of relevant knowledge across different data sources, which depend upon not only the relatedness of these data sources, but also the
target application problem. In this talk we examine how to fuse heterogeneous information to improve mining effectiveness over various
applications, including social network, recommendation, mobile health (m-health) and Question Answering (QA).
