Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning-猫眼课题宝

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2019

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Weishi Shi;Qi Yu

我们提出了一种新的主动学习（AL）模型，该模型集成了贝叶斯和判别核机器，以实现快速准确的多类数据采样。通过加入稀疏贝叶斯模型和统一内核机器委员会（KMC）下的最大边际机，该模型能够识别最能代表整个数据空间的少量数据样本，同时准确地捕捉决策边界。使用最大熵判别框架进行整合，从而得到包含广义熵作为正则化子的联合目标函数。这样的属性允许所提出的AL模型选择更有效地处理不可分离的分类问题的数据样本。参数学习是通过一个原则性的优化框架，利用凸对偶和稀疏结构的KMC有效地优化联合目标函数。关键模型参数用于设计一种新的采样函数，以选择可以同时改善多个决策边界的数据样本，使其成为具有大量类的问题的有效采样器。在合成数据和真实的数据上进行的实验以及与竞争AL方法的比较证明了该模型的有效性。

We propose a novel active learning (AL) model that integrates Bayesian and discriminative kernel machines for fast and accurate multi-class data sampling. By joining a sparse Bayesian model and a maximum margin machine under a unified kernel machine committee (KMC), the proposed model is able to identify a small number of data samples that best represent the overall data space while accurately capturing the decision boundaries. The integration is conducted using the maximum entropy discrimination framework, resulting in a joint objective function that contains generalized entropy as a regularizer. Such a property allows the proposed AL model to choose data samples that more effectively handle non-separable classification problems. Parameter learning is achieved through a principled optimization framework that leverages convex duality and sparse structure of KMC to efficiently optimize the joint objective function. Key model parameters are used to design a novel sampling function to choose data samples that can simultaneously improve multiple decision boundaries, making it an effective sampler for problems with a large number of classes. Experiments conducted over both synthetic and real data and comparison with competitive AL methods demonstrate the effectiveness of the proposed model.

Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning

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