Research Area:
Dimensionality Reduction
In machine learning and statistics, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration, via obtaining a set "uncorrelated" principle variables. It can be divided into feature selection and feature extraction.
Manifold Learning
High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. If the manifold is of low enough dimension, the data can be visualised in the low-dimensional space.
Education
Nanjing UniversitySeptember 2015 - Now
graduate Student
Currently I am a first year graduate student of Department of Computer Science and Technology in Nanjing University and a member of RINC Lab, led by professor Furao Shen.
NanJing University of Posts and Telecommunications September 2011 - June 2015
Undergraduate
With a glorious tradition, Nanjing University of Posts and Telecommunications has made remarkable contributions to the establishment and development of the People’s Republic of China. Its predecessor was Wartime Postal Administration Cadre Training Class of Shandong Anti-Japanese Base, which was established in 1942 and was one of the earliest schools of our Party and army for systematically training telecommunication talents.
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