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Decision Making under Uncertainty: Methodologies from Signal Processing & Forecasting

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Journal Club Overview

This jointly sponsored journal club will bring students from the engineering and business communities together to review seminal literature around decision theory in the context of signal and information processing.

Each session will include three presentations by meeting participants, each lasting 15 minutes with 5 additional minutes of discussion time. The focus of this journal club will be to broaden knowledge base and research scope for PhD students.

The journal articles can be selected from the Centre’s recommended list of articles on decision theory. A list will be emailed to all participants.

Friday Session | 13:00-14:00

Journal Club Topic
Decomposition of large datasets using Bayesian extensions

Applications to Computer Vision and Human Computer Interaction and Brain Computer Interfacing research will be discussed.

Paper Review #1
Sukrit Shankar, PhD Candidate, University of Cambridge, Department of Engineering

Non-negative matrix, tensor factorisation methods and tensor extensions to correlation analysis applied to computer vision. *Sashua, A. & Hazan, T. “Non-negative tensor factorisation with applications to statistics and computer vision.” In IEEE International Conference on Pattern Recognition (2005)

Paper Review #2
Maria Rosario Mestre, PhD Candidate, University of Cambridge, Department of Engineering

*Cichocki, A., Zdunek, R., Phan, A.H. & Amari, S. “Non negative matrix and tensor factorisations – applications to exploratory multi way data analysis and blond source separation.” John Wiley and Sons (2009)

Professor William Fitzgerald

Professor of Applied Statistics and Signal Processing, Department of Engineering, University of Cambridge

Dr Joan Lasenby

Senior Lecturer in Information Engineering, Department of Engineering, University of Cambridge

Michelle Tuveson

Executive Director, Cambridge Centre for Risk Studies, University of Cambridge

Buciu, I. & Nafornita, I. “Non negative matrix factorisation methods for face recognition under extreme lighting conditions.” In IEEE International Symposium on Signals, Circuits and Systems (2009)

Schmidt, M.N. & Mohamed, S. “Probablistic non negative tensor factorisation using Markov chain Monte Carlo.” In EURASIP Conference on Signal Processing (EUSIPCO) (2009)

Kursun, O., Alpaydin, E. & Favorov, O.V. “Canonical correlation analysis using within class coupling.” Pattern Recognition Letters vol. 32, no. 2, pp. 134 – 144 (2011)

Kim, T.K. & Cipolla, R. “Canonical correlation analysis of video volume tensors for action categorisation and detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 31, no. 8, pp. 1415 – 1428 (2009)

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