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:  CT-Learn 
1. Abstract of Cited Paper (BIB001):  This paper investigates a new machine learning strategy called translated learning. Unlike many previous learning tasks, we focus on how to use labeled data from one feature space to enhance the classification of other entirely different learning spaces. For example, we might wish to use labeled text data to help learn a model for classifying image data, when the labeled images are difficult to obtain. An important aspect of translated learning is to build a "bridge" to link one feature space (known as the "source space") to another space (known as the "target space") through a translator in order to migrate the knowledge from source to target. The translated learning solution uses a language model to link the class labels to the features in the source spaces, which in turn is translated to the features in the target spaces. Finally, this chain of linkages is completed by tracing back to the instances in the target spaces. We show that this path of linkage can be modeled using a Markov chain and risk minimization. Through experiments on the text-aided image classification and cross-language classification tasks, we demonstrate that our translated learning framework can greatly outperform many state-of-the-art baseline methods. 
2. Abstract of Cited Paper (BIB002):  Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from other related source domains for learning. While most of the existing works in this area only focused on using the source data with the same structure as the target data, in this paper, we push this boundary further by proposing a heterogeneous transfer learning framework for knowledge transfer between text and images. We observe that for a target-domain classification problem, some annotated images can be found on many social Web sites, which can serve as a bridge to transfer knowledge from the abundant text documents available over the Web. A key question is how to effectively transfer the knowledge in the source data even though the text can be arbitrarily found. Our solution is to enrich the representation of the target images with semantic concepts extracted from the auxiliary source data through a novel matrix factorization method. By using the latent semantic features generated by the auxiliary data, we are able to build a better integrated image classifier. We empirically demonstrate the effectiveness of our algorithm on the Caltech-256 image dataset. 
3. Abstract of Cited Paper (BIB003):  In this paper, we study the problem of transfer learning from text to images in the context of network data in which link based bridges are available to transfer the knowledge between the different domains. The problem of classification of image data is often much more challenging than text data because of the following two reasons: (a) Labeled text data is very widely available for classification purposes. On the other hand, this is often not the case for image data, in which a lot of images are available from many sources, but many of them are often not labeled. (b) The image features are not directly related to semantic concepts inherent in class labels. On the other hand, since text data tends to have natural semantic interpretability (because of their human origins), they are often more directly related to class labels. Therefore, the relationships between the images and text features also provide additional hints for the classification process in terms of the image feature transformations which provide the most effective results. The semantic challenges of image features are glaringly evident, when we attempt to recognize complex abstract concepts, and the visual features often fail to discriminate such concepts. However, the copious availability of bridging relationships between text and images in the context of web and social network data can be used in order to design for effective classifiers for image data. One of our goals in this paper is to develop a mathematical model for the functional relationships between text and image features, so as indirectly transfer semantic knowledge through feature transformations. This feature transformation is accomplished by mapping instances from different domains into a common space of unspecific topics. This is used as a bridge to semantically connect the two heterogeneous spaces. This is also helpful for the cases where little image data is available for the classification process. We evaluate our knowledge transfer techniques on an image classification task with labeled text corpora and show the effectiveness with respect to competing algorithms. 
4. Abstract of Cited Paper (BIB004):  This paper studies a new machine learning strategy called co-transfer learning. Unlike many previous learning problems, we focus on how to use labeled data of different feature spaces to enhance the classification of different learning spaces simultaneously. For instance, we make use of both labeled images and labeled text data to help learn models for classifying image data and text data together. An important component of co-transfer learning is to build different relations to link different feature spaces, thus knowledge can be co-transferred across different spaces. Our idea is to model the problem as a joint transition probability graph. The transition probabilities can be constructed by using the intra-relationships based on affinity metric among instances and the inter-relationships based on co-occurrence information among instances from different spaces. The proposed algorithm computes ranking of labels to indicate the importance of a set of labels to an instance by propagating the ranking score of labeled instances via the random walk with restart. The main contribution of this paper is to (i) propose a co-transfer learning (CT-Learn) framework that can perform learning simultaneously by co-transferring knowledge across different spaces; (ii) show the theoretical properties of the random walk for such joint transition probability graph so that the proposed learning model can be used effectively; (iii) develop an efficient algorithm to compute ranking scores and generate the possible labels for a given instance. Experimental results on benchmark data (image-text and English-Chinese-French classification data sets) have shown that the proposed algorithm is computationally efficient, and effective in learning across different spaces. In the comparison, we find that the classification performance of the CT-Learn algorithm is better than those of the other tested transfer learning algorithms. 
5. Abstract of Cited Paper (BIB005):  This article studies cotransfer learning, a machine learning strategy that uses labeled data to enhance the classification of different learning spaces simultaneously. The authors model the problem as a coupled Markov chain with restart. The transition probabilities in the coupled Markov chain can be constructed using the intrarelationships based on the affinity metric among instances in the same space, and the interrelationships based on co-occurrence information among instances from different spaces. The learning algorithm computes ranking of labels to indicate the importance of a set of labels to an instance by propagating the ranking score of labeled instances via the coupled Markov chain with restart. Experimental results on benchmark data (multiclass image-text and English-Spanish-French classification datasets) have shown that the learning algorithm is computationally efficient, and effective in learning across different spaces.

Literature Review:
Ng et al. BIB005 BIB006 proposed a Co-Transfer Learning (CT-Learn) framework which aims to transfer knowledge between two or more heterogeneous domains. In such case one can have a target domain and several source domains, each represented by different feature spaces. The proposed method uses a co-transfer strategy which builds relations to link the feature spaces, through co-occurrence data, as to co-transfer knowledge amongst them simultaneously. First a joint transition probability graph P, shown in Eq. (2) which was adapted from BIB005 , is constructed using intra-relationships and inter-relationships for all the co-occurrence, labeled, and unlabeled instances across both domains. The intra-relationships are calculated through the affinity of the intrinsic manifold structure between instances of the i th domain. In (2), the diagonal block matrix P (i,i) , which is an n i by n i matrix, indicates these intra-relationships. The off-diagonal block matrix P (i,j) , which is an n i -by-n j matrix, indicates the inter-relationships between the i th and j th domains and is calculated with the co-occurrence data. The weighting parameter λ i,j controls the amount of knowledge to be transferred from the j th instance space to the i th during the learning process. For a standard binary domain transfer learning task λ target,source ≠ 0 and λ source,target = 0 so that knowledge is transferred from the source to the target and not the target to the source. The learning process is modeled as a coupled Markov-chain based random walk with restart BIB001 where each instance is regarded as a node. The proposed method uses this random walk during the learning process on the joint transition probability graph to propagate the ranking score of labeled instances as to calculate the importance of a set of labels to an unlabeled instance. In other words, the proposed method calculates ranking scores of labels and generates the possible labels for a test instance through propagation of these ranking scores. The use of a Markov chain differs from TLRisk BIB002 as for CT-Learn the Markovian principles are used for the learning process while in TLRisk it is used to estimate parameters. Binary and multi-class experiments were performed comparing the proposed CT-Learn against TTI BIB004 , HTLIC BIB003 , and an SVM model trained only on the target data. The results showed the proposed method is statistically superior, as it increased accuracy over the baselines when applied to the experiment's cross-language and text-image classification tasks. (N ,1) . . . N ,2 P (N ,2) . . .