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:  HDANA 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. 
4. Abstract of Cited Paper (BIB004):  In this paper, we propose a novel algorithm of Generalized Joint Distribution Adaptation (G-JDA) for heterogeneous domain adaptation (HDA), which associates and recognizes cross-domain data observed in different feature spaces (and thus with different dimensionality). With the objective to derive a domain-invariant feature subspace for relating source and target-domain data, our G-JDA learns a pair of feature projection matrices (one for each domain), which allows us to eliminate the difference between projected cross-domain heterogeneous data by matching their marginal and class-conditional distributions. We conduct experiments on cross-domain classification tasks using data across different features, datasets, and modalities. We confirm that our G-JDA would perform favorably against state-of-the-art HDA approaches. 
5. Abstract of Cited Paper (BIB005):  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. 
6. Abstract of Cited Paper (BIB006):  Abstract Heterogeneous domain adaptation is a more challenging problem than homogeneous domain adaptation. The transfer effect is not ideally caused by shallow structure which cannot adequately describe the probability distribution and obtain more effective features. In this paper, we propose a heterogeneous domain adaptation network based on autoencoder, in which two sets of autoencoder networks are used to project the source-domain and target-domain data to a shared feature space to obtain more abstractive feature representations. In the last feature and classification layer, the marginal and conditional distributions can be matched by empirical maximum mean discrepancy metric to reduce distribution difference. To preserve the consistency of geometric structure and label information, a manifold alignment term based on labels is introduced. The classification performance can be improved further by making full use of label information of both domains. The experimental results of 16 cross-domain transfer tasks verify that HDANA outperforms several state-of-the-art methods.

Literature Review:
Wang et al. BIB007 recently proposed a semi-supervised HDA method called Heterogeneous Domain Adaptation Network based on Autoencoder (HDANA). Through the use of a Deep Learning BIB004 approach, this method performs symmetric transformation to project the source and target data onto a shared feature space. This process is realized by using two autoencoder networks for the source and target domain data respectively. The authors claim that, by performing such multi-layer non-linear mapping methodology, one can obtain a more abstractive shared feature space which better represents the probability distributions of the data compared to shallow transfer learning methods. Each autoencoder network in this method consists of n + 1 layers, n of which are feature layers and the final layer is the classification layer. At the last feature and classification layer of each autoencoder network, the marginal and conditional distributions are matched by introducing a Maximum Mean Discrepancy (MMD) metric. This is to reduce the cross-domain data distribution differences exhibited in the shared subspace. Along with the aforementioned metric, a manifold alignment term based on label information is also introduced into the objective function. The addition of such term is to preserve the geometric structure and label consistency of the data. This manifold alignment term contains three parts: geometric, similarity, and dissimilarity terms. The geometric term based on graph ensures that the manifold structure of the data within each domain remains unchanged though it does not account for label information. Thus, the similarity term is maximized during optimization for intra-class examples and the dissimilarity term is maximized for inter-class examples. This ensures that instances of the same class have a similar shared feature representation while those from different classes share a different one. These terms allow for a more discriminatory shared feature representation. Finally, to improve classification performance, a loss term for the softmax classifier used in the classification layer is incorporated into the objective function. The output obtained from this loss term is used during optimization. To optimize the terms of the autoencoders and the objective function, the gradient descent method with backpropagation is used to learn updated parameters. For each iteration, new pseudo-labels are calculated for the unlabeled target instances and the process of mapping, classifying, and updating continues until convergence is reached. The output obtained from the final softmax classifier becomes the final predicted labels for the unlabeled target domain data. Experiments were performed for cross-domain object recognition and cross-lingual text categorization. The proposed HDANA was compared with DAMA BIB001 , MMDT BIB002 , SHFA BIB003 , G-JDA BIB005 , TNT BIB006 , SVM trained only on the labeled target data, and an Autoencoder trained only on the labeled target data. The results showed that the proposed method had superior performance over these benchmarks for all of the tests conducted.