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:  HDCC 
1. Abstract of Cited Paper (BIB001):  In this study, the authors study the video annotation problem over heterogeneous domains, in which data from the image source domain and the video target domain is represented by heterogeneous features with different dimensions and physical meanings. A novel feature learning method, called heterogeneous discriminative analysis of canonical correlation (HDCC), is proposed to discover a common feature subspace in which heterogeneous features can be compared. The HDCC utilises discriminative information from the source domain as well as topology information from the target domain to learn two different projection matrices. By using these two matrices, heterogeneous data can be projected onto a common subspace and different features can be compared. They additionally design a group weighting learning framework for multi-domain adaptation to effectively leverage knowledge learned from the source domain. Under this framework, source domain images are organised in groups according to their semantic meanings, and different weights are assigned to these groups according to their relevancies to the target domain videos. Extensive experiments on the Columbia Consumer Video and Kodak datasets demonstrate the effectiveness of their HDCC and group weighting methods.

Literature Review:
Wang et al. BIB002 proposed a framework called Heterogeneous Discriminative Analysis of Canonical Correlation (HDCC) to handle HDA for the application of video annotation, as well as other HTL, tasks. For video annotation tasks, it is often expensive and difficult to collect sufficient labeled videos to build an effective classifier. The goal, in this case, is to use transfer learning to transfer knowledge from a label abundant image source domain to enhance a target video annotation task which, in this case, has no labels. The issue becomes that source image data are represented by static image features while target video data are represented by spatial-temporal video features. Thus, this method adopts Canonical Correlation Analysis (CCA) to create a common subspace using projection matrices for the source and target by incorporating both the discriminative information exhibited in the source, and the topology information in the target. The topology information in the target is explored by taking each target video and splitting it into smaller clips where then a frame is selected randomly, thus creating a static image from the video as to relate to the static source images. The discriminative information is extracted from the source by maximizing the similarity (reducing the variance) of infra-class samples while minimizing the similarities (maximizing the variance) of inter-class samples to create a more discriminative space. From this subspace, we can then apply standard learning algorithms to create a target model. In this work, DASVM BIB001 is used due to the distribution differences between the captured keyframe and the source image. This method initializes the model using the source labeled data and then they are gradually replaced by target instances to learn the final separation hyperplane. Wang et al. BIB002 also extends HDCC by introducing a joint group weighing methodology to learn from multiple heterogeneous sources. The idea behind this is to organize source images into groups in a joint manner based on their semantic meanings rather than origin. Then, weights are assigned to each group of input source data based on their relatedness to the target video. This in turn, allows for selection of more performance enhancing source data which contains greater semantic knowledge for transferring to the target task. The training with these groups can be split into two phases: (1) a classifier is learned for each input source group and (2) weights are assigned and optimization of such generate the target classifier. In the test phase, image frames are extracted from the videos and inputted into the classifier to predict a final label. Experiments were performed for image-aided video annotation which demonstrated the effectiveness of the proposed HDCC and group weighing methods.