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半监督学习的图形构建与b匹配

Graph Construction and b-Matching for Semi-Supervised Learning
课程网址: http://videolectures.net/icml09_jebara_gcm/  
主讲教师: Tony Jebara
开课单位: 哥伦比亚大学
开课时间: 2009-08-26
课程语种: 英语
中文简介:
基于图的半监督学习（SSL）方法在实际的机器学习系统中发挥着越来越重要的作用。基于图的SSL方法中的Acrucial步骤将数据转换为加权图。然而，大多数SSL文献都着重于开发标签推理算法，而没有广泛研究图形构建方法及其对性能的影响。本文提供了一系列图形构造算法的领先半监督方法的实证研究。这些SSL推理算法包括局部和全局一致性（LGC）方法，高斯随机场（GRF）方法，图形转换通过交替最小化（GTAM）方法以及其他技术。研究了图形构造，稀疏化和加权的Severalapproaches，包括流行的k近邻法（kNN）和b匹配方法。与贪婪构造的kNN图形相反，b匹配图形确保图形中的每个节点具有相同数量的边缘并产生平衡或规则图形。人工数据和真实基准数据集的实验结果表明，b匹配产生更强大的图形，因此提供了明显更好的预测精度，而没有计算时间的任何显着变化。
课程简介: Graph based semi-supervised learning (SSL) methods play an increasingly important role in practical machine learning systems. A crucial step in graph based SSL methods is the conversion of data into a weighted graph. However, most of the SSL literature focuses on developing label inference algorithms without extensively studying the graph building method and its effect on performance. This article provides an empirical study of leading semi-supervised methods under a wide range of graph construction algorithms. These SSL inference algorithms include the Local and Global Consistency (LGC) method, the Gaussian Random Field (GRF) method, the Graph Transduction via Alternating Minimization (GTAM) method as well as other techniques. Several approaches for graph construction, sparsification and weighting are explored including the popular k-nearest neighbors method (kNN) and the b-matching method. As opposed to the greedily constructed kNN graph, the b-matched graph ensures each node in the graph has the same number of edges and produces a balanced or regular graph. Experimental results on both artificial data and real benchmark datasets indicate that b-matching produces more robust graphs and therefore provides significantly better prediction accuracy without any significant change in computation time.
关 键 词: 半监督学习; 机器学习; 图形构造算法
课程来源: 视频讲座网
最后编审: 2019-04-23：lxf
阅读次数: 76

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