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Consistent sequential dictionary learning
时间:2018-08-13 22:55    点击:   所属单位:物理与光电工程学院
讲座名称 Consistent sequential dictionary learning
讲座时间 2018-08-16 09:05:00
讲座地点 北校区图书馆西裙楼三楼报告厅
讲座人 A/Prof. Abd-Krim Seghouane
讲座人介绍

Dr. Karim Seghouane is an ARC Future Fellow and a faculty member of the Department of Electrical And Electronic Engineering at the University of Melbourne, Australia. He completed his PhD in Signal Processing and Control from Université Paris Sud (Paris XI), France, in 2003. Before Moving to the University of Melbourne, he was with NICTA (formaly National ICT Australia) Canberra Research Laboratory and the College of Engineering & Computer Science, Australian National University (ANU) from 2005 to 2012. His main research areas of interest are within statistical signal and image processing. He was the general co-chair of the 2014 IEEE Workshop on Statistical Signal Processing held in Gold Cost, Australia, he is a member of the IEEE Signal Processing Society Machine Learning for Signal Processing Technical Committee, and he currently serves as an associate editor of both the IEEE Transactions on Image Processing and the IEEE Transactions on signal Processing.

讲座内容

Algorithms for learning overcomplete dictionaries for sparse signal representation are mostly iterative minimization methods. Such algorithms alternate between a sparse coding stage and a dictionary update stage. For most, however, the notion of consistency of the learned quantities has not been addressed. As an example, the non-consistency of the dictionary learned by K-SVD will be discussed in this presentation. New adaptive dictionary learning algorithms are presented based on the observation that the observed signals can be approximated as a sum of matrices of the same or different ranks. The proposed methods are derived via sequential penalized rank one or K matrix approximation, where a sparisity norm is introduced as a penalty that promotes sparsity. The proposed algorithms use block coordinate descent approach to consistently estimate the unknowns and have the advantage of having simple closed form solutions for both sparse coding and dictionary update stages. The consistency properties of both the estimated sparse code and dictionary atom are discussed. Experimental results are presented on simulated data and on a real functional magnetic resonance imaging (fMRI) dataset from a finger tapping experiment. Results illustrate the performance improvement of the proposed algorithm compared to other existing algorithms.

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