A Hybrid Model-Based and Learning-Based Approach for Classification Using Limited Number of Training Samples
A Hybrid Model-Based and Learning-Based Approach for Classification Using Limited Number of Training Samples
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DOI：
10.1109/ojsp.2021.3135254
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发表时间：
2021-06
影响因子：
2.8
通讯作者：
Alireza Nooraiepour;W. Bajwa;N. Mandayam
中科院分区：
文献类型：
--
作者：
Alireza Nooraiepour;W. Bajwa;N. Mandayam
对于具有已知参数统计模型的物理系统，考虑在给定有限数量的训练数据样本的情况下进行分类的基本任务。基于独立学习和基于统计模型的分类器在使用小型训练集完成分类任务方面面临着重大挑战。具体来说，仅依赖基于物理的统计模型的分类器通常无法正确调整潜在的不可观察参数，从而导致系统行为的表示不匹配。另一方面，基于学习的分类器通常依赖于来自底层物理过程的大量训练数据，这在大多数实际场景中可能不可行。在本文中，提出了一种称为 HyPhyLearn 的混合分类方法，该方法利用基于物理的统计模型和基于学习的分类器。所提出的解决方案基于这样的猜想：HyPhyLearn 将通过融合各自的优势来缓解与基于学习和基于统计模型的分类器的各个方法相关的挑战。所提出的混合方法首先使用可用的（次优）统计估计程序估计不可观测的模型参数，然后使用基于物理的统计模型生成合成数据。然后，将训练数据样本与合成数据合并到基于神经网络的领域对抗训练的基于学习的分类器中。具体来说，为了解决不匹配问题，分类器学习从训练数据和合成数据到公共特征空间的映射。同时，训练分类器在该空间内寻找判别特征，以完成分类任务。介绍了通信系统的两个案例研究（物理层安全和多用户检测），以强调 HyPhyLearn 的实用性。数值结果表明，与现有的独立或混合分类方法相比，所提出的方法带来了重大的分类改进。
The fundamental task of classification given a limited number of training data samples is considered for physicalsystems with known parametric statistical models. The standalone learning-based and statistical model-based classifiers face major challenges towards the fulfillment of the classification task using a small training set. Specifically, classifiers that solely rely on the physics-based statistical models usually suffer from their inability to properly tune the underlying unobservable parameters, which leads to a mismatched representation of the system’s behaviors. Learning-based classifiers, on the other hand, typically rely on a large number of training data from the underlying physical process, which might not be feasible in most practical scenarios. In this paper, a hybrid classification method—termed HyPhyLearn—is proposed that exploits both the physics-based statistical models and the learning-based classifiers. The proposed solution is based on the conjecture that HyPhyLearn would alleviate the challenges associated with the individual approaches of learning-based and statistical model-based classifiers by fusing their respective strengths. The proposed hybrid approach first estimates the unobservable model parameters using the available (suboptimal) statistical estimation procedures, and subsequently use the physics-based statistical models to generate synthetic data. Then, the training data samples are incorporated with the synthetic data in a learning-based classifier that is based on domain-adversarial training of neural networks. Specifically, in order to address the mismatch problem, the classifier learns a mapping from the training data and the synthetic data to a common feature space. Simultaneously, the classifier is trained to find discriminative features within this space in order to fulfill the classification task. Two case studies from communications systems (physical layer security and multi-user detection) are presented in order to highlight the usefulness of HyPhyLearn. Numerical results demonstrate that the proposed approach leads to major classification improvements in comparison to the existing standalone or hybrid classification methods.