Title: Multi-view Data Analytics
Abstract: Conventional unsupervised data analytics techniques have largely focused on processing datasets of single-type data, e.g., one of text, ECG, Sensor Readings and Image data. With increasing digitization, it has become common to have data objects having representations that encompass different "kinds" of information. For example, the same disease condition may be identified through EEG or fMRI data. Thus, a dataset of EEG-fMRI pairs would be considered as a parallel two-view dataset. Datasets of text-image pairs (e.g., a description of a seashore, and an image of it) and text-text pairs (e.g., problem-solution text, or multi-language text from machine translation scenarios) are other common instances of multi-view data. The challenge in multi-view data analytics is about effectively leveraging such parallel multi-view data to perform analytics tasks such as clustering, retrieval and anomaly detection. This talk will cover some emerging trends in processing multi-view parallel data with a focus on exploratory data analytics over them. In addition to providing a high-level view of the area, this talk will cover two recent research publications authored by the speaker, one on multi-view clustering, and another on multi-view dimensionality reduction.
About the Speaker
Dr. Deepak Padmanabhan holds a faculty position in Computer Science at Queen's University Belfast, United Kingdom. He received his B.Tech from Cochin University and his M.Tech and PhD from Indian Institute of Technology Madras, all in Computer Science. His current research interests include data analytics, similarity search, information retrieval and natural language processing. Deepak has published over 40 research papers across major venues in Information and Knowledge Management. His work has led to seven patents from the USPTO. Recently, he authored a book titled "Operators for Similarity Search" which was published by Springer in 2015. A Senior Member of the IEEE and the ACM, he is also the recipient of the INAE Young Engineer Award 2015, an award recognizing scientific work by researchers across engineering disciplines in India. He may be reached at firstname.lastname@example.org.