新科-一种学习稀疏逆协方差矩阵的有效方法][SINCO - An Efficient Greedy Method for Learning Sparse INverse COvariance Matrix]

新科-一种学习稀疏逆协方差矩阵的有效方法 SINCO - An Efficient Greedy Method for Learning Sparse INverse COvariance Matrix


课程网址:	http://videolectures.net/nipsworkshops09_scheinberg_egm/
主讲教师:	Katya Scheinberg
开课单位:	里海大学
开课时间:	2010-01-19
课程语种:	英语
中文简介:	在此，我们提出一个简单的贪婪算法（sinco）来解决这个优化问题。Sinco以贪婪的方式使用坐标提升来解决最初的问题（与它的前辈，如Covesl[10]和Glasso[4]不同），从而自然地保持解决方案的稀疏性。我们的经验结果表明，Sinco比Glasso[4]具有更好的降低误报率的能力（同时在网络足够稀疏时保持类似的真阳性率），因为其贪婪的增量性质。
课程简介:	Herein, we propose a simple greedy algorithm (SINCO) for solving this optimization problem. SINCO solves the primal problem (unlike its predecessors such as COVSEL [10] and glasso [4]), using coordinate ascent, in a greedy manner, thus naturally preserving the sparsity of the solution. As demonstrated by our empirical results, SINCO has better capability in reducing the false-positive error rate (while maintaining similar true positive rate when networks are sufficiently sparse) than glasso [4], because of its greedy incremental nature.
关键词:	优化问题; 新科; 假阳性错误率; 稀疏性; 渐进性
课程来源:	视频讲座网
最后编审:	2020-06-02：张荧(课程编辑志愿者)
阅读次数:	41

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