Results 1 - 10 of 2,379

Estimating Covariances of Locally Stationary Processes: Rates of Convergence of Best Basis Methods

by D. L. Donoho, S. Mallat, R. von Sachs , 1998
"... Mallat, Papanicolaou and Zhang [MPZ98] recently proposed a method for approximating the covariance of a locally stationary process by a covariance which is diagonal in a specially constructed Coifman--Meyer basis of cosine packets. In this paper we extend this approach to estimating the covariance ..."
Abstract - Cited by 28 (10 self) - Add to MetaCart
from sampled data. Our method combines both wavelet shrinkage and cosine-packet best-basis selection in a simple and natural way. The resulting algorithm is fast and automatic. The method has an interpretation as a nonlinear, adaptive form of anisotropic timefrequency smoothing. We introduce a new

Entropy-Based Algorithms For Best Basis Selection

by Ronald R. Coifman, Mladen Victor Wickerhauser - IEEE Transactions on Information Theory , 1992
"... pretations (position, frequency, and scale), and we have experimented with feature-extraction methods that use best-basis compression for front-end complexity reduction. The method relies heavily on the remarkable orthogonality properties of the new libraries. It is obviously a nonlinear transformat ..."
Abstract - Cited by 675 (20 self) - Add to MetaCart
pretations (position, frequency, and scale), and we have experimented with feature-extraction methods that use best-basis compression for front-end complexity reduction. The method relies heavily on the remarkable orthogonality properties of the new libraries. It is obviously a nonlinear

A Complexity Constraint Best-Basis Wavelet Packet Algorithm for Image Compression

by Detlev Marpe, Hans L. Cycon, Wu Li
"... The concept of adapted waveform analysis using a best basis selection out of a predefined library of wavelet packet (WP) bases allows an efficient representation of a signal. These methods usually have the disadvantage of high computational complexity. In this paper we introduce an extension of the ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
of the best-basis method, the complexity constrained best-basis algorithm (CCBB) which allows an adaptive approach, has relatively low complexity and is memory saving. Our CCBB algorithm generates iteratively a scarce library of WP bases by extending a given library according to the energy distribution

Best-Basis Analysis of Broadband Tremor Signals

by Thorsten Bartosch, Peter Steffen
"... Active volcanos usually generate highly nonstationary broadband tremor signals. Short-time shock events with a frequency content of several decades are superimposed to a stationary narrow band continuous tremor. Tremor signals of this type can be observed in the near field of many active volcanos. I ..."
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. In this paper we will demonstrate the analysis of such signals using a specific tremor signal of Mt. Stromboli (Sicily). We used the Best-Basis Algorithm (BBA) in order to compute a spectrogram which is adapted to signal properties on highly different scales. It turns out that the BBA can reveal better fitting

ATOMIC DECOMPOSITION BY BASIS PURSUIT

by Scott Shaobing Chen , David L. Donoho , Michael A. Saunders , 1995
"... The Time-Frequency and Time-Scale communities have recently developed a large number of overcomplete waveform dictionaries -- stationary wavelets, wavelet packets, cosine packets, chirplets, and warplets, to name a few. Decomposition into overcomplete systems is not unique, and several methods for d ..."
Abstract - Cited by 2728 (61 self) - Add to MetaCart
for decomposition have been proposed, including the Method of Frames (MOF), Matching Pursuit (MP), and, for special dictionaries, the Best Orthogonal Basis (BOB). Basis Pursuit (BP) is a principle for decomposing a signal into an "optimal" superposition of dictionary elements, where optimal means having

K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

by Michal Aharon, et al. , 2006
"... In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and inc ..."
Abstract - Cited by 935 (41 self) - Add to MetaCart
signal representations. Given a set of training signals, we seek the dictionary that leads to the best representation for each member in this set, under strict sparsity constraints. We present a new method—the K-SVD algorithm—generalizing the u-means clustering process. K-SVD is an iterative method

A comparative study of energy minimization methods for Markov random fields

by Richard Szeliski, Ramin Zabih, Daniel Scharstein, Olga Veksler, Aseem Agarwala, Carsten Rother, et al. - IN ECCV , 2006
"... One of the most exciting advances in early vision has been the development of efficient energy minimization algorithms. Many early vision tasks require labeling each pixel with some quantity such as depth or texture. While many such problems can be elegantly expressed in the language of Markov Ran ..."
Abstract - Cited by 415 (36 self) - Add to MetaCart
Random Fields (MRF’s), the resulting energy minimization problems were widely viewed as intractable. Recently, algorithms such as graph cuts and loopy belief propagation (LBP) have proven to be very powerful: for example, such methods form the basis for almost all the top-performing stereo methods

Protein homology detection by HMM-HMM comparison

by Johannes Söding - BIOINFORMATICS , 2005
"... Motivation: Protein homology detection and sequence alignment are at the basis of protein structure prediction, function prediction, and evolution. Results: We have generalized the alignment of protein se-quences with a profile hidden Markov model (HMM) to the case of pairwise alignment of profile H ..."
Abstract - Cited by 401 (8 self) - Add to MetaCart
.7, and 3.3 times more good alignments (“balanced ” score> 0.3) than the next best method (COMPASS), and 1.6, 2.9, and 9.4 times more than PSI-BLAST, at the family, super-family, and fold level. Speed: HHsearch scans a query of 200 residues against 3691 domains in 33s on an AMD64 3GHz PC. This is 10

Learning Overcomplete Representations

by Michael S. Lewicki, Terrence J. Sejnowski , 2000
"... In an overcomplete basis, the number of basis vectors is greater than the dimensionality of the input, and the representation of an input is not a unique combination of basis vectors. Overcomplete representations have been advocated because they have greater robustness in the presence of noise, can ..."
Abstract - Cited by 354 (10 self) - Add to MetaCart
In an overcomplete basis, the number of basis vectors is greater than the dimensionality of the input, and the representation of an input is not a unique combination of basis vectors. Overcomplete representations have been advocated because they have greater robustness in the presence of noise, can

Face recognition by independent component analysis

by Marian Stewart Bartlett, Javier R. Movellan, Terrence J. Sejnowski - IEEE Transactions on Neural Networks , 2002
"... Abstract—A number of current face recognition algorithms use face representations found by unsupervised statistical methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. Principal component analysis (PCA) is a popular example of such ..."
Abstract - Cited by 348 (5 self) - Add to MetaCart
Abstract—A number of current face recognition algorithms use face representations found by unsupervised statistical methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. Principal component analysis (PCA) is a popular example
Results 1 - 10 of 2,379