您当前所在位置: 首页 > 学者

蔡登

  • 26浏览

  • 0点赞

  • 0收藏

  • 0分享

  • 0下载

  • 0评论

  • 引用

期刊论文

Scaling up sparse support vector machines by simultaneous feature and sample reduction

暂无

ICML'17: Proceedings of the 34th International Conference on Machine Learning,2017,70():4016–4025 | 2017年08月01日 | doi/10.5555/3305890.3306096

URL:https://dl.acm.org/doi/10.5555/3305890.3306096

摘要/描述

Sparse support vector machine (SVM) is a popular classification technique that can simultaneously learn a small set of the most interpretable features and identify the support vectors. It has achieved great successes in many real-world applications. However, for large-scale problems involving a huge number of samples and extremely high-dimensional features, solving sparse SVM-s remains challenging. By noting that sparse SVMs induce sparsities in both feature and sample spaces, we propose a novel approach, which is based on accurate estimations of the primal and dual optima of sparse SVMs, to simultaneously identify the features and samples that are guaranteed to be irrelevant to the outputs. Thus, we can remove the identified inactive samples and features from the training phase, leading to substantial savings in both the memory usage and computational cost without sacrificing accuracy. To the best of our knowledge, the proposed method is the first static feature and sample reduction method for sparse SVM. Experiments on both synthetic and real datasets (e.g., the kddb dataset with about 20 million samples and 30 million features) demonstrate that our approach significantly outperforms state-of-the-art methods and the speedup gained by our approach can be orders of magnitude.

关键词:

学者未上传该成果的PDF文件,请等待学者更新

我要评论

全部评论 0

本学者其他成果

    同领域成果