Junyuan Hong

CSE PhD Student

Michigan State University

Biography

I am a Ph.D. student at the MSU. My adventures on Computer Science began when I was still an undergraduate in Optics and Optical Engineering, USTC. Since the first program-language course of C in 2011, programming occupies an amount of my time. Impressed by the spirit of the Open-Source community, I started my exploration at GitHub, struggling to become an open-source developer. Now I am working on research work related to machine learning.

Interests

Artificial Intelligence
Machine Learning
Physics

Education

MSc in Computer Science, 2018

University of Science and Technology of China
BSc in Physics, minor in CS., 2015

University of Science and Technology of China

Selected Publications

Disturbance Grassmann Kernels for Subspace-Based Learning

In this paper, we focus on subspace-based learning problems, where data elements are linear subspaces instead of vectors. To handle this kind of data, Grassmann kernels were proposed to measure the space structure and used with classifiers, e.g., Support Vector Machines (SVMs). However, the existing discriminative algorithms mostly ignore the instability of subspaces, which would cause the classifiers to be misled by disturbed instances. Thus we propose considering all potential disturbances of subspaces in learning processes to obtain more robust classifiers.

Junyuan Hong, Huanhuan Chen, Feng Lin

In KDD’18, 2018

PDF Video DOI Supplementary file