杜鹃线性代数

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2015-08-10 DOI:10.1145/2783258.2783263

Li Zhou, D. Andersen, Mu Li, Alex Smola

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引用次数: 1

摘要

本文提出了一种基于布谷鸟哈希的稀疏向量数据结构。它具有很高的内存效率，并允许以接近密集向量水平的速率随机访问。这使我们能够精确地解决稀疏l1编程问题，而无需预处理，其成本在内存和速度方面与密集线性代数相同。我们的方法为哈希内核提供了一种可行的替代方案，并且在需要精确解决方案时，例如特征选择时，它都表现出色。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Cuckoo Linear Algebra

In this paper we present a novel data structure for sparse vectors based on Cuckoo hashing. It is highly memory efficient and allows for random access at near dense vector level rates. This allows us to solve sparse l1 programming problems exactly and without preprocessing at a cost that is identical to dense linear algebra both in terms of memory and speed. Our approach provides a feasible alternative to the hash kernel and it excels whenever exact solutions are required, such as for feature selection.

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来源期刊

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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