Learning with Augmented Features for Heterogeneous Domain Adaptation (1206.4660v1)

Abstract: We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. Using two different projection matrices, we first transform the data from two domains into a common subspace in order to measure the similarity between the data from two domains. We then propose two new feature mapping functions to augment the transformed data with their original features and zeros. The existing learning methods (e.g., SVM and SVR) can be readily incorporated with our newly proposed augmented feature representations to effectively utilize the data from both domains for HDA. Using the hinge loss function in SVM as an example, we introduce the detailed objective function in our method called Heterogeneous Feature Augmentation (HFA) for a linear case and also describe its kernelization in order to efficiently cope with the data with very high dimensions. Moreover, we also develop an alternating optimization algorithm to effectively solve the nontrivial optimization problem in our HFA method. Comprehensive experiments on two benchmark datasets clearly demonstrate that HFA outperforms the existing HDA methods.

• The paper presents a novel HFA approach that transforms heterogeneous features into a shared subspace to boost cross-domain classification accuracy.
• It employs dual projection matrices and an alternating optimization strategy within an SVM framework to efficiently map and reduce feature dimensions.
• Experimental results on object recognition and multilingual text datasets highlight significant performance gains over traditional domain adaptation methods.

Analysis of "Learning with Augmented Features for Heterogeneous Domain Adaptation"

The paper "Learning with Augmented Features for Heterogeneous Domain Adaptation" by Lixin Duan, Dong Xu, and Ivor W. Tsang presents a novel approach to tackle the problem of heterogeneous domain adaptation (HDA). The authors propose a method called Heterogeneous Feature Augmentation (HFA), which addresses the challenge of adapting models between source and target domains characterized by features of different dimensions. In contrast to traditional domain adaptation methods that typically operate under the assumption of homogeneous feature spaces, this paper introduces an innovative strategy to bridge distinct feature representations effectively.

Methodology Overview

The core of the proposed HFA method lies in transforming and augmenting features from both the source and target domains into a shared subspace using two projection matrices. The key steps involve:

1. Transformation to Common Subspace: The paper utilizes projection matrices $P$ and $Q$ to map heterogeneous features into a common subspace, enabling comparison between the source and target domain data.
2. Augmented Feature Mapping: Two new feature mapping functions are introduced to augment the transformed data with original features, enhancing the representation power while maintaining correspondences across domains.
3. Optimization Strategy: The paper adopts a robust optimization strategy using Support Vector Machines (SVM) to solve the adaptation problem. The authors define a transformation metric, $H$, which simplifies the optimization process by reducing the dimensionality within which the solution is sought, avoiding direct computation of the projection matrices $P$ and $Q$.
4. Kernelization: To handle high dimensional data efficiently, the method is augmented with a kernel trick that enables nonlinear transformations, thus enhancing the generalization capabilities of the model for complex datasets.
5. Alternating Optimization Algorithm: The proposed method includes an alternating optimization algorithm to iteratively solve the dual problem of the SVM and the optimization of the transformation metric $H$.

Experimental Results

The authors conducted comprehensive experiments on two benchmarking datasets: an object recognition dataset and the Reuters multilingual text categorization dataset. The results clearly demonstrate the superiority of the HFA approach over several baseline methods, including Kernel Canonical Correlation Analysis (KCCA), manifold alignment methods, and other existing HDA approaches. Notably, the HFA method achieves significant improvements in classification accuracies by leveraging augmented heterogeneous feature representations.

Implications and Future Directions

The proposed HFA method introduces a practical approach to exploit disparate domain feature sets effectively and can be integrated into various machine learning frameworks that require adaptation across heterogeneous domains. This advancement implies potential applications in areas such as cross-lingual text analysis, multi-modal data fusion, and adaptive computer vision systems.

Future work could explore enhancing the flexibility of the feature augmentation process and extending the method to multi-target domain settings. Investigating alternative feature augmentation strategies and exploring efficient computational techniques to further reduce the complexity of the transformation metric, especially for large-scale datasets, are promising directions. Additionally, extending the framework to unsupervised and semi-supervised domain adaptation scenarios could widen its applicability across an even broader range of machine learning problems.