You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A survey on heterogeneous transfer learning 
Chapter Title:  MMDT 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  Abstract: We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers. Specifically, we form a linear transformation that maps features from the target (test) domain to the source (training) domain as part of training the classifier. We optimize both the transformation and classifier parameters jointly, and introduce an efficient cost function based on misclassification loss. Our method combines several features previously unavailable in a single algorithm: multi-class adaptation through representation learning, ability to map across heterogeneous feature spaces, and scalability to large datasets. We present experiments on several image datasets that demonstrate improved accuracy and computational advantages compared to previous approaches.

Literature Review:
Hoffman et al. BIB003 proposed a heterogeneous domain adaptation method for multi-class image classification called Max-Margin Domain Transforms (MMDT). This method allows for multiple classes, requires limited labeled target training instances, and utilizes unlabeled target instances though one can have classes which contain only unlabeled instances. This is possible as it adapts all the points through a linear asymmetric feature transformation to map the target domain to that of the source creating a feature transformation shared across all classes. To establish multi-class support, the proposed method adapts a max-margin classifier by learning a shared component of the domain shift though the feature transformation. MMDT simultaneously learns the projection matrices along with the classifier parameters and performs optimization on both through a classification loss based cost function. In other words, in this framework both the classification objective and the feature transformation are updated together based on the prediction results of the previous training instance in relation to its truth label. By performing such optimization through classification loss, accuracy can be increased. This method also provides for less computational cost as it reduces training time by using hyperplane, rather than similarity, constraints. By doing so, one can optimize in linear space and avoid computational complexities of kernel techniques, thus making the proposed method suitable for scaling to larger quantities of training data. The main idea behind this method is to jointly learn affine hyperplanes which separate the classes consisting of source and target instances. This is done while learning the feature transformation to map the points of the target to the source such that it projects the points onto the correct side of each source hyperplane. In comparison, ARC-t BIB001 uses similarity constraints to map points of the same category close to each other followed by a classification step but, on the other hand, MMDT proposes to project the target points to the correct side of the already learned hyperplane as for better classification accuracy. Experiments were performed to test the performance of the proposed method for image classification tasks. Four tests were conducted which include multi-class, homogeneous domain adaption, heterogeneous domain adaption, multi-task DA, as well as scaling the test to larger datasets for computational performance analysis. For the heterogeneous tests, the proposed MMDT was compared with ARC-t BIB001 , HFA BIB002 , and SVM trained only on the target data. The results showed that the proposed method was effective and it had the highest classification accuracy in most cases, while also having the fastest computation time for the larger dataset test while maintaining highest accuracy.