Dictionary Learning

作者：Ivana Tošić, Pascal Frossard · 发表于：IEEE Signal Processing Magazine · 年份：2011 · DOI：10.1109/msp.2010.939537 · 被引用次数：792 · 研究领域：Sparse and Compressive Sensing Techniques、Blind Source Separation Techniques、Neural Networks and Applications

We describe methods for learning dictionaries that are appropriate for the representation of given classes of signals and multisensor data. We further show that dimensionality reduction based on dictionary representation can be extended to address specific tasks such as data analy sis or classification when the learning includes a class separability criteria in the objective function. The benefits of dictionary learning clearly show that a proper understanding of causes underlying the sensed world is key to task-specific representation of relevant information in high-dimensional data sets.