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-SVM 
1. Abstract of Cited Paper (BIB001):  We present a novel domain adaptation approach for solving cross-domain pattern recognition problems, i.e., the data or features to be processed and recognized are collected from different domains of interest. Inspired by canonical correlation analysis (CCA), we utilize the derived correlation subspace as a joint representation for associating data across different domains, and we advance reduced kernel techniques for kernel CCA (KCCA) if nonlinear correlation subspace are desirable. Such techniques not only makes KCCA computationally more efficient, potential over-fitting problems can be alleviated as well. Instead of directly performing recognition in the derived CCA subspace (as prior CCA-based domain adaptation methods did), we advocate the exploitation of domain transfer ability in this subspace, in which each dimension has a unique capability in associating cross-domain data. In particular, we propose a novel support vector machine (SVM) with a correlation regularizer, named correlation-transfer SVM, which incorporates the domain adaptation ability into classifier design for cross-domain recognition. We show that our proposed domain adaptation and classification approach can be successfully applied to a variety of cross-domain recognition tasks such as cross-view action recognition, handwritten digit recognition with different features, and image-to-text or text-to-image classification. From our empirical results, we verify that our proposed method outperforms state-of-the-art domain adaptation approaches in terms of recognition performance.

Literature Review:
Yeh et al. BIB002 proposed a framework for cross-domain pattern recognition tasks which uses Kernel CCA for symmetric feature transformation and proposed a modified SVM named Correlation-Transfer SVM (we denote as CT-SVM). This method utilizes Canonical Correlation Analysis (CCA) BIB001 for deriving a domain-invariant joint feature space to associate data from the source and target. Here, CCA aims to maximize the correlation, through a coefficient ρ, between two variable sets and learns projection vectors to derive a subspace which can be used as a common feature representation to solve cross-domain problems. Reduced Kernel CCA is utilized when non-linear subspaces are desired. The common subspace dimension is bounded by the minimum feature dimension whether it from the source or target feature space as to prevent overfitting. Once this common subspace is derived, one can project unseen test data onto the subspace for classification by a model trained from the source view data, as in this case no labeled target instances are available. Rather than directly using this subspace, this method proposes to exploit the domain adaption ability that can be observed in the subspace. Each dimension in the derived CCA is associated to its own ρ value. The higher the correlation coefficient ρ, the closer the source and target domains are to each other in such dimension thus it is more suited for domain transfer. An example of this can be seen in Fig. 4 which was inspired from BIB002 . As illustrated in Fig. 4a , in the dimension with the higher ρ value the source and target data are more similar and their points on the subspace are more clustered together. Therefore, a classifier projected onto the subspace will have an easier time discriminating between the classes for the unseen target test data. On the other hand, in Fig. 4b , in the dimension with the lower ρ value the source and target are dissimilar, as there is a higher difference in distribution, which will result in lower recognition performance. Standard CCA-based approaches for Fig. 4 Illustration for CT-SVM, inspired from BIB002 , showing the difference between a ρ value with higher correlation (a) versus a ρ value with lower correlation (b) when projecting source and target instances onto correlation subspaces cross-domain tasks do not take the domain transferability into account when learning classifiers in the correlation subspace which can lead to degraded performance when lower ρ values are present. This method thus proposes using a modified SVM called Correlation-Transfer SVM which takes such transferability into account by enforcing a suppression of the learned SVM along such dimensions with lower ρ values as they have little or no beneficial knowledge for transfer to the target task. This, in effect, will provide more focus on the attributes with higher correlation between the source and target domains where beneficial discriminatory knowledge is more likely to be transferred from. This method does not require labeled target data and it utilizes the available unlabeled target and source pairs to select a kernel parameter which results in the highest correlation performance. Experiments were performed for cross-view action recognition, handwritten digit recognition, and image-to-text/text-to-image classification to analyze the effectiveness of the proposed algorithm. The proposed method was compared with combinations of linear and kernel SVM and CCA where the proposed method was shown to be effective. The results also indicate that deriving a common subspace better suited for domain adaption is more important than designing a complex classifier in a standard feature space.