A survey on heterogeneous transfer learning <s> SHFA <s> Labeled examples are often expensive and time-consuming to obtain. One practically important problem is: can the labeled data from other related sources help predict the target task, even if they have (a) different feature spaces (e.g., image vs. text data), (b) different data distributions, and (c) different output spaces? This paper proposes a solution and discusses the conditions where this is possible and highly likely to produce better results. It works by first using spectral embedding to unify the different feature spaces of the target and source data sets, even when they have completely different feature spaces. The principle is to cast into an optimization objective that preserves the original structure of the data, while at the same time, maximizes the similarity between the two. Second, a judicious sample selection strategy is applied to select only those related source examples. At last, a Bayesian-based approach is applied to model the relationship between different output spaces. The three steps can bridge related heterogeneous sources in order to learn the target task. Among the 12 experiment data sets, for example, the images with wavelet-transformed-based features are used to predict another set of images whose features are constructed from color-histogram space. By using these extracted examples from heterogeneous sources, the models can reduce the error rate by as much as~50\%, compared with the methods using only the examples from the target task. <s> BIB001 </s> A survey on heterogeneous transfer learning <s> SHFA <s> We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications. <s> BIB002 </s> A survey on heterogeneous transfer learning <s> SHFA <s> In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features can change from one dataset to the next. In this paper, we address the problem of visual domain adaptation for transferring object models from one dataset or visual domain to another. We introduce ARC-t, a flexible model for supervised learning of non-linear transformations between domains. Our method is based on a novel theoretical result demonstrating that such transformations can be learned in kernel space. Unlike existing work, our model is not restricted to symmetric transformations, nor to features of the same type and dimensionality, making it applicable to a significantly wider set of adaptation scenarios than previous methods. Furthermore, the method can be applied to categories that were not available during training. We demonstrate the ability of our method to adapt object recognition models under a variety of situations, such as differing imaging conditions, feature types and codebooks. <s> BIB003 </s> A survey on heterogeneous transfer learning <s> SHFA <s> 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. <s> BIB004 </s> A survey on heterogeneous transfer learning <s> SHFA <s> In this paper, we study the heterogeneous domain adaptation (HDA) problem, in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. By introducing two different projection matrices, we first transform the data from two domains into a common subspace such that the similarity between samples across different domains can be measured. We then propose a new feature mapping function for each domain, which augments the transformed samples with their original features and zeros. Existing supervised learning methods ( e.g., SVM and SVR) can be readily employed by incorporating our newly proposed augmented feature representations for supervised HDA. As a showcase, we propose a novel method called Heterogeneous Feature Augmentation (HFA) based on SVM. We show that the proposed formulation can be equivalently derived as a standard Multiple Kernel Learning (MKL) problem, which is convex and thus the global solution can be guaranteed. To additionally utilize the unlabeled data in the target domain, we further propose the semi-supervised HFA (SHFA) which can simultaneously learn the target classifier as well as infer the labels of unlabeled target samples. Comprehensive experiments on three different applications clearly demonstrate that our SHFA and HFA outperform the existing HDA methods. <s> BIB005
Li et al. BIB005 directly extended the HFA BIB004 algorithm and proposed Semi-supervised Heterogeneous Feature Augmentation (SHFA). This method exploits the knowledge from unlabeled target instances to enhance a target HTL task with limited target labels. The method utilizes ρ-SVM with squared hinge loss trained on the limited target as to infer pseudo-labels for the unlabeled instances. The task of finding the optimal labels is computationally expensive and is denoted as a Mixed Integer Programming problem. Because of this, an optimal linear combination of the feasible labeling candidates is found for the instances leading to a less expensive optimization problem. These pseudolabels are used during the building of the final classifier and are estimated when learning the optimal nonlinear transformation metric H. As described in Duan BIB004 and "HFA", H is a combination of the projections P and Q used to map the source and target into a common subspace and by using H one does not need to solve for P and Q directly but rather only optimize the H transformation matrix. For SHFA, H is decomposed into a linear combination of a set of rank-one positive semi-definite matrices. Optimization of such is then solved using Multiple Kernel Learning as defined in  with the l 1 -norm constraint. Experiments were performed for object recognition, text categorization, and sentiment classification. The proposed SHFA was compared with the original HFA BIB004 , HeMap BIB001 , DAMA BIB002 , ARC-t BIB003 , and an SVM trained only on the labeled target instances. The proposed SHFA performed significantly better than the other baselines for these experiments by having better classification accuracy, including better performance over standard HFA.