TY - GEN
T1 - Iteratively reweighted least squares algorithms for L1-Norm principal component analysis
AU - Park, Young Woong
AU - Klabjan, Diego
PY - 2017/1/31
Y1 - 2017/1/31
N2 - 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. L1 PCA uses the L1 norm to measure error, whereas the conventional PCA uses the L2 norm. For the L1 PCA problem minimizing the fitting error of the reconstructed data, we propose an exact reweighted and an approximate algorithm based on iteratively reweighted least squares. We provide convergence analyses, and compare their performance against benchmark algorithms in the literature. The computational experiment shows that the proposed algorithms consistently perform best.
AB - 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. L1 PCA uses the L1 norm to measure error, whereas the conventional PCA uses the L2 norm. For the L1 PCA problem minimizing the fitting error of the reconstructed data, we propose an exact reweighted and an approximate algorithm based on iteratively reweighted least squares. We provide convergence analyses, and compare their performance against benchmark algorithms in the literature. The computational experiment shows that the proposed algorithms consistently perform best.
UR - http://www.scopus.com/inward/record.url?scp=85014527801&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85014527801&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2016.14
DO - 10.1109/ICDM.2016.14
M3 - Conference contribution
AN - SCOPUS:85014527801
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 430
EP - 438
BT - Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
A2 - Bonchi, Francesco
A2 - Wu, Xindong
A2 - Baeza-Yates, Ricardo
A2 - Domingo-Ferrer, Josep
A2 - Zhou, Zhi-Hua
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Data Mining, ICDM 2016
Y2 - 12 December 2016 through 15 December 2016
ER -