Online Learning for Group Lasso

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Online Learning for Group Lasso
We develop a novel online learning algorithm for the group lasso in order to efficiently find the important explanatory factors in a grouped manner. Different from traditional batch-mode group lasso algorithms, which suffer from the inefficiency and poor scalability, our proposed algorithm performs in an online mode and scales well: at each iteration one can update the weight vector according to a closed-form solution based on the average of previous subgradients. Therefore, the proposed online algorithm can be very efficient and scalable. This is guaranteed by its low worst-case time complexity and memory cost both in the order of O(d), where d is the number of dimensions. Moreover, in order to achieve more sparsity in both the group level and the individual feature level, we successively extend our online system to efficiently solve a number of variants of sparse group lasso models. We also show that the online system is applicable to other group lasso models, such as the group lass...
Haiqin Yang, Zenglin Xu, Irwin King, Michael R. Ly
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICML
Authors Haiqin Yang, Zenglin Xu, Irwin King, Michael R. Lyu
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