压缩非负稀疏编码

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI:10.1109/ICDM.2010.162

Fei Wang, Ping Li

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引用次数: 9

摘要

稀疏编码(SC)将数据向量建模为基向量上的稀疏线性组合，已广泛应用于机器学习、信号处理和神经科学等领域。针对非负稀疏编码(non - negative Sparse Coding, NSC)的小内存问题，提出了一种双随机投影方法。实际数据实验证明了该方法的有效性。

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Compressed Nonnegative Sparse Coding

Sparse Coding (SC), which models the data vectors as sparse linear combinations over basis vectors, has been widely applied in machine learning, signal processing and neuroscience. In this paper, we propose a dual random projection method to provide an efficient solution to Nonnegative Sparse Coding (NSC) using small memory. Experiments on real world data demonstrate the effectiveness of the proposed method.

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2010 IEEE International Conference on Data Mining

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