IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is a scholarly archival journal published monthly. This journal covers traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence. Read the full scope of TPAMI.
From the September 2015 issue
Learning Efficient Sparse and Low Rank Models
By P. Sprechmann, A. M. Bronstein, and G. Sapiro
Parsimony, including sparsity and low rank, has been shown to successfully model data in numerous machine learning and signal processing tasks. Traditionally, such modeling approaches rely on an iterative algorithm that minimizes an objective function with parsimony-promoting terms. The inherently sequential structure and data-dependent complexity and latency of iterative optimization constitute a major limitation in many applications requiring real-time performance or involving large-scale data. Another limitation encountered by these modeling techniques is the difficulty of their inclusion in discriminative learning scenarios. In this work, we propose to move the emphasis from the model to the pursuit algorithm, and develop a process-centric view of parsimonious modeling, in which a learned deterministic fixed-complexity pursuit process is used in lieu of iterative optimization. We show a principled way to construct learnable pursuit process architectures for structured sparse and robust low rank models, derived from the iteration of proximal descent algorithms. These architectures learn to approximate the exact parsimonious representation at a fraction of the complexity of the standard optimization methods. We also show that appropriate training regimes allow to naturally extend parsimonious models to discriminative settings. State-of-the-art results are demonstrated on several challenging problems in image and audio processing with several orders of magnitude speed-up compared to the exact optimization algorithms.
Editorials and Announcements
- According to Thomson Reuters' 2013 Journal Citation Report, TPAMI has an impact factor of 5.694.
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- We are pleased to announce that David Forsyth, a professor at the University of Illinois at Urbana-Champaign, is the new Editor in Chief of IEEE Transactions on Pattern and Machine Intelligence starting in 2013. He was previously a member of the advisory board of TPAMI.
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- State of the Journal (Jan 2015)
- Editor's Note (June 2013)
- Farewall State of the Journal (Jan 2013)
- Editor's Note (Jan 2013)
- Editor's Note (May 2012)
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- Special Issue on Higher Order Graphical Models in Computer Vision (July 2015)
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- Special Section on Learning Deep Architectures (Aug 2013)
- In Memoriam: Mark Everingham (Nov 2012)
- Introduction to the Special Section on IEEE Conference on Computer Vision and Pattern Recognition (September 2012)
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