Multi-View Learning Task

See: Learning Task, Multi-View SVM.

References

(Sun, 2013) ⇒ Shiliang Sun. (2013). “A Survey of Multi-view Machine Learning." Neural Computing and Applications 23, no. 7-8
- ABSTRACT: Multi-view learning or learning with multiple distinct feature sets is a rapidly growing direction in machine learning with well theoretical underpinnings and great practical success. This paper reviews theories developed to understand the properties and behaviors of multi-view learning and gives a taxonomy of approaches according to the machine learning mechanisms involved and the fashions in which multiple views are exploited. This survey aims to provide an insightful organization of current developments in the field of multi-view learning, identify their limitations, and give suggestions for further research. One feature of this survey is that we attempt to point out specific open problems which can hopefully be useful to promote the research of multi-view machine learning.
- KEYWORDS: Multi-view learning; Statistical learning theory; Canonical correlation analysis; Co-training; Co-regularization

(Zhang et al., 2011) ⇒ Dan Zhang, Jingrui He, Yan Liu, Luo Si, and Richard Lawrence. (2011). “Multi-view Transfer Learning with a Large Margin Approach.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020593
- QUOTE: Transfer learning has been proposed to address the problem of scarcity of labeled data in the target domain by leveraging the data from the source domain. In many real world applications, data is often represented from different perspectives, which correspond to multiple views. For example, a web page can be described by its contents and its associated links. However, most existing transfer learning methods fail to capture the [[multi-view {nature}]], and might not be best suited for such applications.

(Ng & Gong, 1999) ⇒ Jeffrey Ng, and Shaogang Gong. (1999). “Multi-view Face Detection and Pose Estimation Using a Composite Support Vector Machine Across the View Sphere.” In: ratfg-rts, p. 14. IEEE,
- QUOTE: … Understanding the face pose distribution across the view sphere can provide a basis for learning a generic face model based on multi-view support vector machines. … Positive SVs 107 139 176 190 203 Table 1. The division of the view sphere for learning multi-view SVMs. ...