Compression of two-dimensional data
IEEE Transactions on Information Theory
COLT '90 Proceedings of the third annual workshop on Computational learning theory
The weighted majority algorithm
Information and Computation
Machine Learning - Special issue on context sensitivity and concept drift
The Cost of Achieving the Best Portfolio in Hindsight
Mathematics of Operations Research
Tracking a small set of experts by mixing past posteriors
The Journal of Machine Learning Research
Prediction, Learning, and Games
Prediction, Learning, and Games
Parameterized Duration Mmodeling for Switching Linear Dynamic Systems
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Efficient adaptive algorithms and minimax bounds for zero-delay lossy source coding
IEEE Transactions on Signal Processing
Universal Switching Linear Least Squares Prediction
IEEE Transactions on Signal Processing
Switching Linear Dynamical Systems for Noise Robust Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Coding for a binary independent piecewise-identically-distributed source
IEEE Transactions on Information Theory - Part 2
IEEE Transactions on Information Theory
Low-complexity sequential lossless coding for piecewise-stationary memoryless sources
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Universal discrete denoising: known channel
IEEE Transactions on Information Theory
Universal denoising for the finite-input general-output channel
IEEE Transactions on Information Theory
Universal Minimax Discrete Denoising Under Channel Uncertainty
IEEE Transactions on Information Theory
Schemes for Bidirectional Modeling of Discrete Stationary Sources
IEEE Transactions on Information Theory
Universal Zero-Delay Joint Source–Channel Coding
IEEE Transactions on Information Theory
Universal Filtering Via Prediction
IEEE Transactions on Information Theory
Scanning and Sequential Decision Making for Multidimensional Data–Part I: The Noiseless Case
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Universal Algorithms for Channel Decoding of Uncompressed Sources
IEEE Transactions on Information Theory
How to Filter an “Individual Sequence With Feedback”
IEEE Transactions on Information Theory
Scanning and Sequential Decision Making for Multidimensional Data—Part II: The Noisy Case
IEEE Transactions on Information Theory
Universal Denoising of Discrete-Time Continuous-Amplitude Signals
IEEE Transactions on Information Theory
IEEE Transactions on Signal Processing
A universal scheme for Wyner-Ziv coding of discrete sources
IEEE Transactions on Information Theory
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We introduce S-DUDE, a new algorithm for denoising discrete memoryless channel (DMC)-corrupted data. The algorithm, which generalizes the recently introduced DUDE (Discrete Universal DEnoiser), aims to complete with a genie that has access, in addition to the noisy data, also to the underlying clean data, and that can choose to switch, up to m times, between sliding-window denoisers in a way that minimizes the overall loss. When the underlying data form an individual sequence, we show that the S-DUDE performs essentially as well as this genie, provided that m is sublinear in the size of the data. When the clean data are emitted by a piecewise stationary process, we show that the S-DUDE achieves the optimum distribution-dependent performance, provided that the same sublinearity condition is imposed on the number of switches. To further substantiate the universal optimality of the S-DUDE, we show that when the number of switches is allowed to grow linearly with the size of the data, any (sequence of) scheme(s) fails to compete in the above sense. Using dynamic programming, we derive an efficient implementation of the S-DUDE, which has complexity (time and memory) growing linearly with the data size and the number of switches m. Preliminary experimental results are presented, suggesting that S-DUDE has the capacity to improve on the performance attained by the original DUDE in applications where the nature of the data abruptly changes in time (or space), as is often the case in practice.