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低阶判别分析的流体动力学模型

Fluid dynamics models for low rank discriminant analysis
课程网址: http://videolectures.net/aistats2010_noh_fdmfl/  
主讲教师: Yung-Kyun Noh
开课单位: 首尔国立大学
开课时间: 信息不详。欢迎您在右侧留言补充。
课程语种: 英语
中文简介:
我们考虑了为分类而减少标注数据维数的问题。不幸的是,寻找最小贝叶斯分类误差的低维投影的最优方法是难以解决的,因此大多数标准算法在投影子空间中优化一个易于处理的启发式函数。在这里,我们研究一个基于物理的模型,我们认为标记的数据是相互作用的流体分布。我们从信息论势函数推导出流体中所产生的力,并考虑对所产生的加速度和速度流场的适当的低秩约束。我们展示了如何应用高斯最小约束原理在流体中获得低阶投影的可处理解。为了更好地逼近高斯系统的贝叶斯最优解,本文提出了一种流体动力学方法。
课程简介: We consider the problem of reducing the dimensionality of labeled data for classification. Unfortunately, the optimal approach of finding the low-dimensional projection with minimal Bayes classification error is intractable, so most standard algorithms optimize a tractable heuristic function in the projected subspace. Here, we investigate a physics-based model where we consider the labeled data as interacting fluid distributions. We derive the forces arising in the fluids from information theoretic potential functions, and consider appropriate low rank constraints on the resulting acceleration and velocity flow fields. We show how to apply the Gauss principle of least constraint in fluids to obtain tractable solutions for low rank projections. Our fluid dynamic approach is demonstrated to better approximate the Bayes optimal solution on Gaussian systems, including infinite dimensional Gaussian processes.
关 键 词: 低阶判别分析; 流体动力学模型
课程来源: 视频讲座网
最后编审: 2019-10-30:cwx
阅读次数: 39