Young Woong Park
Northwestern University
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Publication
Featured researches published by Young Woong Park.
international conference on data mining | 2016
Young Woong Park; Diego Klabjan
Principal component analysis (PCA) is often used to reduce the dimension of data by selecting a few orthonormal vectors that explain most of the variance structure of the data.
Discrete Applied Mathematics | 2015
Young Woong Park; Diego Klabjan
Transportation Research Record | 2014
Christopher Lindsey; Andreas Frei; Hani S. Mahmassani; Young Woong Park; Diego Klabjan; Michael Reed; Gregory Langheim; Todd Keating
L_1
Informs Journal on Computing | 2017
Young Woong Park; Yan Jiang; Diego Klabjan; Loren Williams
Discrete Applied Mathematics | 2015
Sergey Shebalov; Young Woong Park; Diego Klabjan
L1 PCA uses the
Machine Learning | 2016
Young Woong Park; Diego Klabjan
Archive | 1996
Young Woong Park; Clayton O. Ruud; Paul H. Cohen
L_1
Archive | 1995
Young Woong Park; Paul H. Cohen; Clayton O. Ruud
arXiv: Machine Learning | 2013
Young Woong Park; Diego Klabjan
L1 norm to measure error, whereas the conventional PCA uses the
arXiv: Machine Learning | 2017
Seokhyun Chung; Young Woong Park; Taesu Cheong