Transfer Learning for Cross-Dataset Recognition: A Survey <s> Labelled Plus Unlabelled Target Dataset <s> When labeled examples are limited and difficult to obtain, transfer learning employs knowledge from a source domain to improve learning accuracy in the target domain. However, the assumption made by existing approaches, that the marginal and conditional probabilities are directly related between source and target domains, has limited applicability in either the original space or its linear transformations. To solve this problem, we propose an adaptive kernel approach that maps the marginal distribution of target-domain and source-domain data into a common kernel space, and utilize a sample selection strategy to draw conditional probabilities between the two domains closer. We formally show that under the kernel-mapping space, the difference in distributions between the two domains is bounded; and the prediction error of the proposed approach can also be bounded. Experimental results demonstrate that the proposed method outperforms both traditional inductive classifiers and the state-of-the-art boosting-based transfer algorithms on most domains, including text categorization and web page ratings. In particular, it can achieve around 10% higher accuracy than other approaches for the text categorization problem. The source code and datasets are available from the authors. <s> BIB001 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Labelled Plus Unlabelled Target Dataset <s> While domain adaptation (DA) aims to associate the learning tasks across data domains, heterogeneous domain adaptation (HDA) particularly deals with learning from cross-domain data which are of different types of features. In other words, for HDA, data from source and target domains are observed in separate feature spaces and thus exhibit distinct distributions. In this paper, we propose a novel learning algorithm of Cross-Domain Landmark Selection (CDLS) for solving the above task. With the goal of deriving a domain-invariant feature subspace for HDA, our CDLS is able to identify representative cross-domain data, including the unlabeled ones in the target domain, for performing adaptation. In addition, the adaptation capabilities of such cross-domain landmarks can be determined accordingly. This is the reason why our CDLS is able to achieve promising HDA performance when comparing to state-of-the-art HDA methods. We conduct classification experiments using data across different features, domains, and modalities. The effectiveness of our proposed method can be successfully verified. <s> BIB002
Compared to the scenario where only limited labelled target data are presented, additional redundant unlabelled target domain data are also presented in the training stage in this problem, which allows the algorithms to learn the structure information of target domain. This setting is realistic in real world applications, where unlabelled data are much more easier to obtain than labelled data. The characteristics of prob-lem 3.2 are shown in Table II , which is known as semi-supervised domain adaptation among the literature. Hybrid Knowledge Transfer. BIB001 propose an adaptive kernel approach that maps the marginal distributions of target and source domain data into a common kernel space, and utilize a sample selection strategy to draw conditional probabilities between the two domains closer, where the statistic criterion is used for both the feature transformation and instance re-weighting. In this problem, both limited labelled and sufficient unlabelled target data are presented (Table IX) , which is named semi-supervised heterogeneous domain adaptation. Hybrid Knowledge Transfer. BIB002 propose a learning algorithm of Cross-Domain Landmark Selection (CDLS) for solving heterogeneous domain adaptation (HDA) using the statistic criterion (MMD), where the instance weights and feature transformation are learnt simultaneously. Specifically, the CDLS derives a heterogeneous feature transformation which results in a domain-invariant subspace for associating cross-domain data, and assign weight to each instance according to their adaptation ability using both labelled and unlabelled target samples.