Domain adaptation (DA) is transfer learning which aims to leverage labeled data in a related source domain to achieve informed knowledge transfer and help the classification of unlabeled data in a target domain. In this paper, we propose a novel DA method, namely Robust Data Geometric Structure Aligned, Close yet Discriminative Domain Adaptation (RSA-CDDA), which brings closer, in a latent joint subspace, both source and target data distributions, and aligns inherent hidden source and target data geometric structures while performing discriminative DA in repulsing both interclass source and target data. The proposed method performs domain adaptation between source and target in solving a unified model, which incorporates data distribution constraints, in particular via a nonparametric distance, i.e., Maximum Mean Discrepancy (MMD), as well as constraints on inherent hidden data geometric structure segmentation and alignment between source and target, through low rank and sparse representation. RSA-CDDA achieves the search of a joint subspace in solving the proposed unified model through iterative optimization, alternating Rayleigh quotient algorithm and inexact augmented Lagrange multiplier algorithm. Extensive experiments carried out on standard DA benchmarks, i.e., 16 cross-domain image classification tasks, verify the effectiveness of the proposed method, which consistently outperforms the state-of-the-art methods.
State of the art in DA has featured so far two main approaches: 1) the search of a novel joint subspace where source and target domain have a similar data distribution @cite_15 @cite_7 @cite_8 ; or 2) adaptation of the prediction model trained using the source data @cite_4 @cite_16 @cite_5 . In the first approach, source and target data are changed because projected into the novel joint subspace. A prediction model trained on labeled source data in this novel subspace can thus be applied to projected target data because they are aligned or have similar data distribution in the novel joint subspace. The second approach is to modify the parameters of the prediction model trained on the source domain so that the decision boundaries are adapted to the target data which remain unchanged.
@cite_4 @cite_7 @cite_8 @cite_5 @cite_15 @cite_16
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This paper presents a novel technique for addressing domain adaptation (DA) problems with active learning (AL) in the classification of remote sensing images. DA models the important problem of adapting a supervised classifier trained on a given image (source domain) to the classification of another similar but not identical image (target domain) acquired on a different area. The main idea of the proposed approach is iteratively labeling and adding to the training set the minimum number of the most informative samples from the target domain, while removing the source-domain samples that do not fit with the distributions of the classes in the target domain. In this way, the classification system exploits already available information, i.e., the labeled samples of source domain, in order to minimize the number of target domain samples to be labeled, thus reducing the cost associated to the definition of the training set for the classification of the target domain. In addition, we define a convergence criterion that allows the technique to stop the iterative AL process on the target domain without relying on the availability of a test set for it. This is an important contribution, as in operational applications, it is not realistic to assume that a test set for the target domain is available. Experimental results obtained in the classification of very high resolution and hyperspectral images confirm the effectiveness of the proposed technique. Domain adaptation (DA) has gained a lot of success in the recent years in computer vision to deal with situations where the learning process has to transfer knowledge from a source to a target domain. In this paper, we introduce a novel unsupervised DA approach based on both subspace alignment and selection of landmarks similarly distributed between the two domains. Those landmarks are selected so as to reduce the discrepancy between the domains and then are used to non linearly project the data in the same space where an efficient subspace alignment (in closed-form) is performed. We carry out a large experimental comparison in visual domain adaptation showing that our new method outperforms the most recent unsupervised DA approaches. Heterogeneous domain adaptation aims to exploit labeled training data from a source domain for learning prediction models in a target domain under the condition that the two domains have different input feature representation spaces. In this paper, we propose a novel semi-supervised subspace co-projection method to address multi-class heterogeneous domain adaptation. The proposed method projects the instances of the two domains into a co-located latent subspace to bridge the feature divergence gap across domains, while simultaneously training prediction models in the co-projected representation space with labeled training instances from both domains. It also exploits the unlabeled data to promote the consistency of co-projected subspaces from the two domains based on a maximum mean discrepancy criterion. Moreover, to increase the stability and discriminative informativeness of the subspace co-projection, we further exploit the error-correcting output code schemes to incorporate more binary prediction tasks shared across domains into the learning process. We formulate this semi-supervised learning process as a non-convex joint minimization problem and develop an alternating optimization algorithm to solve it. To investigate the empirical performance of the proposed approach, we conduct experiments on cross-lingual text classification and cross-domain digit image classification tasks with heterogeneous feature spaces. The experimental results demonstrate the efficacy of the proposed method on these heterogeneous domain adaptation problems. We present a novel unsupervised domain adaptation (DA) method for cross-domain visual recognition. Though subspace methods have found success in DA, their performance is often limited due to the assumption of approximating an entire dataset using a single low-dimensional subspace. Instead, we develop a method to effectively represent the source and target datasets via a collection of low-dimensional subspaces, and subsequently align them by exploiting the natural geometry of the space of subspaces, on the Grassmann manifold. We demonstrate the effectiveness of this approach, using empirical studies on two widely used benchmarks, with state of the art domain adaptation performance