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Research Topic: Feature Extraction

Feature extraction is one of the most widely employed methods for reducing the data dimensionality. There are two major categories of dimensionality reduction techniques. The first category utilizes an unsupervised setting, with Principal Component Analysis being one of the most well-known methods. The second category employs the supervised setting, with Linear Discriminant Analysis being one of the most well-known methods.

I mainly study the relationships among different kinds of unsupervised and supervised dimensionality reduction methods, revealing their theoretical relationships and evaluating their empirical performance.


My published papers on this topic:

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