TRANSFER LEARNING BASED ON LOGISTIC REGRESSION
TRANSFER LEARNING BASED ON LOGISTIC REGRESSION
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DOI：
10.5194/isprsarchives-xl-3-w3-145-2015
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发表时间：
2015-08
期刊：
影响因子：
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通讯作者：
A. Paul;F. Rottensteiner;C. Heipke
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文献类型：
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作者：
A. Paul;F. Rottensteiner;C. Heipke
摘要。本文以领域自适应为重点，在迁移学习框架下研究了遥感图像的分类问题。主要的新贡献是一种基于逻辑回归的遥感转导迁移学习方法。逻辑回归是一种计算复杂度低的判别概率分类器，可以处理多类问题。这个研究领域涉及的方法是解决这样的问题，即假设标记的训练数据集只适用于源域，而在具有不同但相关特征的目标域需要分类。分类是用超平面的权系数模型进行的，超平面在转换后的特征空间中分离特征。在逻辑回归方面，我们的领域自适应方法通过对目标测试数据集进行迭代标记来调整模型参数。这些标记的数据特征被迭代地添加到当前的训练集中，在开始时，该训练集中只包含源特征，同时，从当前的训练集中删除一些源特征。基于合成和真实数据的一系列测试的实验结果构成了该方法的第一个概念验证。
Abstract. In this paper we address the problem of classification of remote sensing images in the framework of transfer learning with a focus on domain adaptation. The main novel contribution is a method for transductive transfer learning in remote sensing on the basis of logistic regression. Logistic regression is a discriminative probabilistic classifier of low computational complexity, which can deal with multiclass problems. This research area deals with methods that solve problems in which labelled training data sets are assumed to be available only for a source domain, while classification is needed in the target domain with different, yet related characteristics. Classification takes place with a model of weight coefficients for hyperplanes which separate features in the transformed feature space. In term of logistic regression, our domain adaptation method adjusts the model parameters by iterative labelling of the target test data set. These labelled data features are iteratively added to the current training set which, at the beginning, only contains source features and, simultaneously, a number of source features are deleted from the current training set. Experimental results based on a test series with synthetic and real data constitutes a first proof-of-concept of the proposed method.