[see Linear regression with random projections, 2012 for corrections]
Odalric-Ambrym Maillard, Rémi Munos.
In NIPS ’09, pages 1213–1221, 2009.
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Abstract: |
We consider the problem of learning, from K data, a regression function in a linear space of high dimension N using projections onto a random subspace of lower dimension M. From any algorithm minimizing the (possibly penalized) empirical risk, we provide bounds on the excess risk of the estimate computed in the projected subspace (compressed domain) in terms of the excess risk of the estimate built in the high-dimensional space (initial domain). We show that solving the problem in the compressed domain instead of the initial domain reduces the estimation error at the price of an increased (but controlled) approximation error. We apply the analysis to Least-Squares (LS) regression and discuss the excess risk and numerical complexity of the resulting “Compressed Least Squares Regression” (CLSR) in terms of N, K, and M. When we choose M=O(\sqrt{K}), we show that CLSR has estimation error of order O(\log K / \sqrt{K}) and provides an interesting alternative to usual penalization techniques where one first selects a random subspace of lower dimension and then performs an empirical risk minimizer in this subspace. |
You can dowload the paper from the NIPS Website (here) or from the HAL online open depository* (here).
Bibtex: |
@incollection{MaillardM09, title = {Compressed Least-Squares Regression}, author = {Odalric Maillard and R\'{e}mi Munos}, booktitle = {Advances in Neural Information Processing Systems 22}, editor = {Y. Bengio and D. Schuurmans and J.D. Lafferty and C.K.I. Williams and A. Culotta}, pages = {1213–1221}, year = {2009}, publisher = {Curran Associates, Inc.}, url = {http://papers.nips.cc/paper/3698-compressed-least-squares-regression.pdf} } |