Project 60: Advanced Kernel Methods for Machine Learning
Contact Information:
Prof. Xiaolin Huang
Email: xiaolinhuang@sjtu.edu.cn
Project Description and Objectives:
Kernel methods, which implicitly maps data into feature spaces, are very important in machine learning and have been widely applied in many fields. In recent years, the success of deep learning implies that enhancing the flexibility with the support of big data is promising to improve machine learning performance. The route is also applicable to advancing kernel methods, which are traditionally restricted to shallow structures.
In this project, we will investigate several key issues of advanced kernel methods. First, it is necessary to design a deeper structure, with several nonlinear layers, and develop the corresponding training methods. Second, making kernels flexible usually violates positive definiteness condition that is usually required by classical kernels, and an investigation of indefinite kernel methods is desirable. Third, flexible kernels need to admit value-defined matrices, for which the out-of-sample extension technique is necessary.
The objectives of this project consist of:
1) Novel kernel methods in one of the three topics: deep kernel/indefinite kernel/out-of-sample extension;
2) A toolbox for the developed techniques.
Eligibility Requirements:
Basic knowledge on machine learning.
Programming skills in MATLAB, Python, C.
Main Tasks:
Develop novel machine learning methods based on flexible kernels.
Establish and release a toolbox for the developed methods.
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