Uncertainty in Artificial Intelligence
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Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent
Yichao Lu, Dean Foster
Abstract:
We propose a new two stage algorithm LING for large scale regression problems. LING has the same risk as the well known Ridge Regres- sion under the fixed design setting and can be computed much faster. Our experiments have shown that LING performs well in terms of both prediction accuracy and computational efficiency compared with other large scale regression al- gorithms like Gradient Descent, Stochastic Gra- dient Descent and Principal Component Regres- sion on both simulated and real datasets.
Keywords:
Pages: 525-532
PS Link:
PDF Link: /papers/14/p525-lu.pdf
BibTex:
@INPROCEEDINGS{Lu14,
AUTHOR = "Yichao Lu and Dean Foster",
TITLE = "Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "525--532"
}


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