TY - GEN
T1 - Efficient Euclidean projections in linear time
AU - Liu, Jun
AU - Ye, Jieping
PY - 2009/12/9
Y1 - 2009/12/9
N2 - We consider the problem of computing the Euclidean projection of a vector of length n onto a closed convex set including the ℓ1 and the specialized polyhedra employed in (Shalev-Shwartz & Singer, 2006). These problems have played building block roles in solving several ℓ1-norm based sparse learning problems. Existing methods have a worst-case time complexity of O(n log n). In this paper, we propose to cast both Euclidean projections as root finding problems associated with specific auxiliary functions, which can be solved in linear time via bisection. We further make use of the special structure of the auxiliary functions, and propose an improved bisection algorithm. Empirical studies demonstrate that the proposed algorithms are much more efficient than the competing ones for computing the projections.
AB - We consider the problem of computing the Euclidean projection of a vector of length n onto a closed convex set including the ℓ1 and the specialized polyhedra employed in (Shalev-Shwartz & Singer, 2006). These problems have played building block roles in solving several ℓ1-norm based sparse learning problems. Existing methods have a worst-case time complexity of O(n log n). In this paper, we propose to cast both Euclidean projections as root finding problems associated with specific auxiliary functions, which can be solved in linear time via bisection. We further make use of the special structure of the auxiliary functions, and propose an improved bisection algorithm. Empirical studies demonstrate that the proposed algorithms are much more efficient than the competing ones for computing the projections.
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M3 - Conference contribution
AN - SCOPUS:71149115443
SN - 9781605585161
T3 - Proceedings of the 26th International Conference On Machine Learning, ICML 2009
SP - 657
EP - 664
BT - Proceedings of the 26th International Conference On Machine Learning, ICML 2009
T2 - 26th International Conference On Machine Learning, ICML 2009
Y2 - 14 June 2009 through 18 June 2009
ER -