Abstract
Recently, Learning Machines have achieved a measure of success in the representation of multiple views. Since the effectiveness of data mining methods is highly dependent on the ability to produce data representation, learning multi-visual representation has become a very promising topic with widespread use. It is an emerging data mining guide that looks at multidisciplinary learning to improve overall performance. Multi-view reading is also known as data integration or data integration from multiple feature sets. In general, learning the representation of multiple views is able to learn the informative and cohesive representation that leads to the improvement in the performance of predictors. Therefore, learning multi-view representation has been widely used in many real-world applications including media retrieval, native language processing, video analysis, and a recommendation program. We propose two main stages of learning multidisciplinary representation: (i) alignment of multidisciplinary representation, which aims to capture relationships between different perspectives on content alignment; (ii) a combination of different visual representations, which seeks to combine different aspects learned from many different perspectives into a single integrated representation. Both of these strategies seek to use the relevant information contained in most views to represent the data as a whole. In this project we use the concept of canonical integration analysis to get more details. Encouraged by the success of in-depth reading, in-depth reading representation of multiple theories has attracted a lot of attention in media access due to its ability to read explicit visual representation.