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:  TNT 
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. 
3. Abstract of Cited Paper (BIB003):  In this paper, we present an efficient multi-class heterogeneous domain adaptation method, where data from source and target domains are represented by heterogeneous features of different dimensions. Specifically, we propose to reconstruct a sparse feature transformation matrix to map the weight vector of classifiers learned from the source domain to the target domain. We cast this learning task as a compressed sensing problem, where each binary classifier induced from multiple classes can be deemed as a measurement sensor. Based on the compressive sensing theory, the estimation error of the transformation matrix decreases with the increasing number of classifiers. Therefore, to guarantee reconstruction performance, we construct sufficiently many binary classifiers based on the error correcting output coding. Extensive experiments are conducted on both a toy dataset and three real-world datasets to verify the superiority of our proposed method over existing state-of-the-art HDA methods in terms of prediction accuracy. 
4. Abstract of Cited Paper (BIB004):  We present a novel approach to enrich classification trees with the representation learning ability of deep (neural) networks within an end-to-end trainable architecture. We combine these two worlds via a stochastic and differentiable decision tree model, which steers the formation of latent representations within the hidden layers of a deep network. The proposed model differs from conventional deep networks in that a decision forest provides the final predictions and it differs from conventional decision forests by introducing a principled, joint and global optimization of split and leaf node parameters. Our approach compares favourably to other state-of-the-art deep models on a large-scale image classification task like ImageNet. 
5. Abstract of Cited Paper (BIB005):  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. 
6. Abstract of Cited Paper (BIB006):  Heterogeneous domain adaptation (HDA) addresses the task of associating data not only across dissimilar domains but also described by different types of features. Inspired by the recent advances of neural networks and deep learning, we propose Transfer Neural Trees (TNT) which jointly solves cross-domain feature mapping, adaptation, and classification in a NN-based architecture. As the prediction layer in TNT, we further propose Transfer Neural Decision Forest (Transfer-NDF), which effectively adapts the neurons in TNT for adaptation by stochastic pruning. Moreover, to address semi-supervised HDA, a unique embedding loss term for preserving prediction and structural consistency between target-domain data is introduced into TNT. Experiments on classification tasks across features, datasets, and modalities successfully verify the effectiveness of our TNT.

Literature Review:
Chen et al. BIB007 proposed a neural network based framework called Transfer Neural Trees (TNT) for semi-supervised HDA tasks. This framework is divided into two layers: mapping and prediction. The first layer is that of mapping the source and target into a domain-invariant representation while the second layer performs adaptation and classification. This process of mapping, adaptation and classification are all solved in a joint manner. Mapping of the source domain data is performed separately to that of the target domain data while each using a single-layer neural network. For the case of this study, these neural networks apply the hyperbolic tangent as the activation function with an output dimension of 100. To account for the unlabeled target instances in semi-supervised tasks, an embedding loss term is incorporated into the target domain feature mapping. When performing adaptation and classification, minimizing this loss term can increase predictive consistency for the outputs of the individual trees and the forest as a whole. This process also preserves the structural consistency between the labeled and unlabeled target instances. For adaptation and classification, Chen et al. BIB007 also proposed Transfer Neural Decision Forest (Transfer-NDF) for use in the TNT framework. Inspired from deep neural decision forest BIB005 and random forest BIB001 , Transfer-NDF uses neural networks as decision trees as to build a forest of neural decision trees. To build this forest, first the source domain data is observed to build the individual trees. Both the source mapping and the forest are updated via backpropagation. Then, once the trees are obtained, the target domain data is observed to perform distribution adaptation. Also, during the learning of Transfer-NDF, a process of stochastic pruning is applied which adapts representative neurons for better generalization from the learned source domain to that of the target. In the case of this study, Transfer-NDF consists of 20 trees with a depth of 7. Similar to the process of Random Forest, each tree samples 20 dimensions from the final mapping output for a diverse representation of the data. Experiments were performed for cross-domain object recognition and text-to-image classification. The proposed TNT method was compared with MMDT BIB003 , HFA BIB002 , SHFR BIB004 , SCP-ECOC BIB006 , dNDF BIB005 , SVM trained only on the labeled target data, and a two-layer neural network trained only on the labeled target data. The proposed method outperformed these baselines for almost all of the experiments thus demonstrating its effectiveness for these tasks.