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Heterogeneous Domain Adaptation via Nonlinear Matrix Factorization

机译：非线性矩阵分解的异构域自适应

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摘要

Heterogeneous domain adaptation (HDA) aims to solve the learning problems where the source- and the target-domain data are represented by heterogeneous types of features. The existing HDA approaches based on matrix completion or matrix factorization have proven to be effective to capture shareable information between heterogeneous domains. However, there are two limitations in the existing methods. First, a large number of corresponding data instances between the source domain and the target domain are required to bridge the gap between different domains for performing matrix completion. These corresponding data instances may be difficult to collect in real-world applications due to the limited size of data in the target domain. Second, most existing methods can only capture linear correlations between features and data instances while performing matrix completion for HDA. In this paper, we address these two issues by proposing a new matrix-factorization-based HDA method in a semisupervised manner, where only a few labeled data are required in the target domain without requiring any corresponding data instances between domains. Such labeled data are more practical to obtain compared with cross-domain corresponding data instances. Our proposed algorithm is based on matrix factorization in an approximated reproducing kernel Hilbert space (RKHS), where nonlinear correlations between features and data instances can be exploited to learn heterogeneous features for both the source and the target domains. Extensive experiments are conducted on cross-domain text classification and object recognition, and experimental results demonstrate the superiority of our proposed method compared with the state-of-the-art HDA approaches.
机译：异构域自适应（HDA）旨在解决学习问题，其中源域和目标域数据由异类特征表示。事实证明，基于矩阵完成或矩阵分解的现有HDA方法可有效捕获异构域之间的可共享信息。但是，现有方法有两个局限性。首先，在源域和目标域之间需要大量相应的数据实例，以弥合不同域之间的间隙以执行矩阵完成。由于目标域中数据的大小有限，在实际的应用程序中可能很难收集这些对应的数据实例。其次，大多数现有方法只能在执行HDA矩阵完成时捕获特征和数据实例之间的线性相关性。在本文中，我们通过半监督的方式提出了一种新的基于矩阵分解的HDA方法来解决这两个问题，该方法在目标域中仅需要少量标记数据，而在域之间不需要任何相应的数据实例。与跨域对应的数据实例相比，此类标记的数据更实用。我们提出的算法基于近似再现内核希尔伯特空间（RKHS）中的矩阵分解，可以利用特征和数据实例之间的非线性相关性来学习源域和目标域的异构特征。在跨域文本分类和对象识别方面进行了广泛的实验，实验结果表明，与最新的HDA方法相比，我们提出的方法具有优越性。

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