Comparison of basis selection methods

Adler, J; Rao, Bhaskar D.; Kreutz-Delgado, Kenneth

doi:10.1109/acssc.1996.600867

Cited by 46 publications

(40 citation statements)

References 11 publications

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“…Sometimes this is also referred to as the readmission problem [44]. There are several ways to compensate for these problems (see for example [39], [44]- [47]). …”

Section: Relaxation Of the Nls Estimatormentioning

confidence: 99%

On perceptual distortion minimization and nonlinear least-squares frequency estimation

Christensen

Jensen

2006

IEEE Trans. Audio Speech Lang. Process.

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“…Sometimes this is also referred to as the readmission problem [44]. There are several ways to compensate for these problems (see for example [39], [44]- [47]). …”

Section: Relaxation Of the Nls Estimatormentioning

confidence: 99%

On perceptual distortion minimization and nonlinear least-squares frequency estimation

Christensen

Jensen

2006

IEEE Trans. Audio Speech Lang. Process.

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“…A group of methods is based on first selecting the best vector, then the vector that together with the selected one produce best fit, and so on. Such an approach is termed the forward greedy selection approach, and the majority of the proposed methods are related to this type [3], [12], [32], [35]. Another approach, the backward greedy selection approach, starts with the full dictionary, and sequentially eliminates one vector after the other [13], [14], [40].…”

Section: A Overviewmentioning

confidence: 99%

“…The inputs for the P-frame prediction model are as follows: [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] the same as for the previous I-frames but applied on P-frames 17) previous P-frame minus mean of P-frames 18) difference between previous 2 P-frames 19) mean of previous 2 P-frames 20)…”

Section: )mentioning

confidence: 99%

Sparse Basis Selection: New Results and Application to Adaptive Prediction of Video Source Traffic

Atiya

Aly

Parlos³

2005

IEEE Trans. Neural Netw.

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Abstract-Real-time prediction of video source traffic is an important step in many network management tasks such as dynamic bandwidth allocation and end-to-end quality-of-service (QoS) control strategies. In this paper, an adaptive prediction model for MPEG-coded traffic is developed. A novel technology is used, first developed in the signal processing community, called sparse basis selection. It is based on selecting a small subset of inputs (basis) from among a large dictionary of possible inputs. A new sparse basis selection algorithm is developed that is based on efficiently updating the input selection adaptively. When a new measurement is received, the proposed algorithm updates the selected inputs in a recursive manner. Thus, adaptability is not only in the weight adjustment, but also in the dynamic update of the inputs. The algorithm is applied to the problem of single-step-ahead prediction of MPEG-coded video source traffic, and the developed method achieves improved results, as compared to the published results in the literature. The present analysis indicates that the adaptive feature of the developed algorithm seems to add significant overall value.Index Terms-Internet traffic, MPEG, sparse basis, sparse representation, video traffic prediction.

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“…The system of equations (1) has infinitely many solutions, and the solution set is a linear variety denoted by LV A; b = xp + NA, where xp is any particular solution to (1) and NA = Nullspace of A. Constrained minimization of diversity measures results in sparse solutions consistent with membership in LV A; b.…”

Section: Introductionmentioning

confidence: 99%

“…There has been considerable recent interest in the issue of best basis selection for sparse signal representation, including approaches that select basis vectors by minimizing diversity measures subject to the constraint Ax = b; (1) where A is an m n matrix formed using the vectors from an overdetermined dictionary of basis vectors, m n , and it is assumed that rankA = m [3,13,1,10].…”

Section: Introductionmentioning

confidence: 99%

Measures and algorithms for best basis selection

Kreutz-Delgado

Rao

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181

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A general framework based on majorization, Schur-concavity, and concavity is given that facilitates the analysis of algorithm performance and clarifies the relationships between existing proposed diversity measures useful for best basis selection. Admissible sparsity measures are given by the Schur-concave functions, which are the class of functions consistent with the partial ordering on vectors known as majorization. Concave functions form an important subclass of the Schur-concave functions which attain their minima at sparse solutions to the basis selection problem. Based on a particular functional factorization of the gradient, we give a general affine scaling optimization algorithm that converges to a sparse solution for measures chosen from within this subclass.

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Comparison of basis selection methods

Cited by 46 publications

References 11 publications

On perceptual distortion minimization and nonlinear least-squares frequency estimation

On perceptual distortion minimization and nonlinear least-squares frequency estimation

Sparse Basis Selection: New Results and Application to Adaptive Prediction of Video Source Traffic

Measures and algorithms for best basis selection

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