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:  CDLS 
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):  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.

Literature Review:
Tsai et al. BIB004 proposed a semi-supervised HDA solution called Cross-Domain Landmark Selection (CDLS). This method derives a domain-invariant common subspace and learns representative cross-domain landmarks for adaptation purposes. To initialize the process, first all the target domain data is projected into an m-dimensional subspace. Here, m ≤ min(d s , d t ) as to prevent overfitting from mapping a low dimensional space into a higher one. Then a feature transformation A is learned to project the source data onto the m-dimensional space creating a common subspace for the source and target data. A linear SVM is trained on the labeled cross-domain data to predict pseudo-labels for the unlabeled target instances. Once this is initialized the optimization process begins which updates the transformation A and landmark weights {α, β}. Then, with these new landmark weights, an updated SVM is trained to predict new pseudo-labels for the unlabeled target instances. This process repeats until convergence is reached. From this, final predicted labels are the output thus exploiting unlabeled target data during training. These landmark weights are assigned to instances which are most representative of the labeled target instances and thus are more suited to be used for adaption. The higher the α, the more similar a labeled source instance is to a labeled target instance of the same class. The higher the β, the more similar an unlabeled target instance is. Those with lower weights have a higher chance to be misclassified. From this observation, one should use the instances with the higher weights for adaptation purposes as they are most representative of the target domain thus filtering out noisy examples. A parameter δ ∊ [0, 1] is then introduced to set the cut-off point for the portion of source labeled and target unlabeled instances to be used. If δ = 0, then no cross-domain data will be used and the classifier will be trained only on the available target data. Note that when δ = 0, this a supervised variant of CDLS used in the experiment called CDLS_sup. If δ = 1, then all the cross-domain data will be used including those with low weights. Both of these are non-optimal as the first exploits no cross-domain data and the latter uses too much which introduces noisy examples, thus δ = 0.5 was used in the study. Experiments were performed for object recognition and cross-lingual text categorization. The proposed method was compared with SHFA BIB003 , DAMA BIB001 , MMDT BIB002 , CDLS_sup, and an SVM trained only on the labeled target data. The proposed method performed significantly better for all tests.