Learning With Label Proportions via NPSVM
Learning With Label Proportions via NPSVM
复制标题
通过 NPSVM 学习标签比例
DOI：
10.1109/tcyb.2016.2598749
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发表时间：
2016
影响因子：
11.8
通讯作者：
Fan Meng
中科院分区：
文献类型：
--
作者：
Zhiquan Qi;Bo Wang;Liufeng Niu;Fan Meng
最近，从标签比例（LLP），寻求广义的实例级预测器仅仅基于袋级标签比例的学习，引起了广泛的兴趣。然而，由于其弱标签的情况下，LLP通常福尔斯到一个转导的学习框架占一个棘手的组合优化问题。在本文中，我们提出了一个全新的算法，称为LLP通过非并行支持向量机（LLP-NPSVM），以促进这一困境。为了获得满意的数据自适应性，该方案在标签比例信息的监督下，根据两个非平行的超平面确定实例标签，而不是直推式学习方式。在几何视图中，我们的方法可以被解释为一种替代的竞争方法，受益于大利润聚类。在实践中，LLP-NPSVM可以有效地解决通过应用两个快速顺序最小优化路径迭代。为了合理地支持我们的方法的有效性，有限终止和单调下降的建议LLP-NPSVM过程进行了本质上的分析。各种实验表明，我们的算法具有快速收敛性和强大的数值稳定性，沿着最好的精度在最近开发的几种方法在大多数情况下。
Recently, learning from label proportions (LLPs), which seeks generalized instance-level predictors merely based on bag-level label proportions, has attracted widespread interest. However, due to its weak label scenario, LLP usually falls into a transductive learning framework accounting for an intractable combinatorial optimization issue. In this paper, we propose a brand new algorithm, called LLPs via nonparallel support vector machine (LLP-NPSVM), to facilitate this dilemma. To harness satisfactory data adaption, instead of transductive learning fashion, our scheme determined instance labels according to two nonparallel hyper-planes under the supervision of label proportion information. In a geometrical view, our approach can be interpreted as an alternative competitive method benefiting from large margin clustering. In practice, LLP-NPSVM can be efficiently addressed by applying two fast sequential minimal optimization paths iteratively. To rationally support the effectiveness of our method, finite termination and monotonic decrease of the proposed LLP-NPSVM procedure were essentially analyzed. Various experiments demonstrated our algorithm enjoys rapid convergence and robust numerical stability, along with best accuracies among several recently developed methods in most cases.