Optimization of code book in vector quantization

Thangavel, K.; Kumar, D. Ashok

doi:10.1007/s10479-006-7391-0

Cited by 14 publications

(6 citation statements)

References 5 publications

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“…Since for our data an optimum code book was estimated to have code words, we used a standard heuristic and applied the k -means algorithm with k = 20 over the space of N P = 31609 contact vectors to identify the elements in A * . Cluster algorithms like k -means approximate a given set of many feature vectors by a much smaller number of representative vectors [28]. Algorithmic convergence was reached rapidly and resulted in a set of twenty code words A * = { a * 1 , …, a * 20 }, where each a * i ∈ A * was a single contact vector.…”

Section: Methodsmentioning

confidence: 99%

Random Amino Acid Mutations and Protein Misfolding Lead to Shannon Limit in Sequence-Structure Communication

Lisewski

2008

PLoS ONE

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The transmission of genomic information from coding sequence to protein structure during protein synthesis is subject to stochastic errors. To analyze transmission limits in the presence of spurious errors, Shannon's noisy channel theorem is applied to a communication channel between amino acid sequences and their structures established from a large-scale statistical analysis of protein atomic coordinates. While Shannon's theorem confirms that in close to native conformations information is transmitted with limited error probability, additional random errors in sequence (amino acid substitutions) and in structure (structural defects) trigger a decrease in communication capacity toward a Shannon limit at 0.010 bits per amino acid symbol at which communication breaks down. In several controls, simulated error rates above a critical threshold and models of unfolded structures always produce capacities below this limiting value. Thus an essential biological system can be realistically modeled as a digital communication channel that is (a) sensitive to random errors and (b) restricted by a Shannon error limit. This forms a novel basis for predictions consistent with observed rates of defective ribosomal products during protein synthesis, and with the estimated excess of mutual information in protein contact potentials.

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Section: Methodsmentioning

confidence: 99%

Random Amino Acid Mutations and Protein Misfolding Lead to Shannon Limit in Sequence-Structure Communication

Lisewski

2008

PLoS ONE

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“…This section will elaborate on the possibility of taking advantage of the extra information by using a more flexible approach. Artificial neural networks (ANN) have been widely used as universal function approximators and, as another data mining techniques, have found plenty of room in operations research (Thangavel and Kumar 2006;Teodorovic et al 2006;Schwardt and Fischer 2009;Jiao et al 2009;Menache et al 2005;Hao et al 2004;Kainen et al 2001;Józefowska et al 2001;Ivanova and Tagarev 2000;Perantonis et al 2000;Charalambous et al 2000). Their most common architecture for that purpose consists of an input layer that captures the values of the different predictors, a number of hidden layers that interconnect the entire set of values of the predictors, and an output layer that condenses the different signals to produce the estimates.…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

Effort estimates through project complexity

et al. 2010

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This paper reports the results obtained from use of project complexity parameters in modeling effort estimates. It highlights the attention that complexity has recently received in the project management area. After considering that traditional knowledge has consistently proved to be prone to failure when put into practice on actual projects, the paper endorses the belief that there is a need for more open-minded and novel approaches to project management. With a view to providing some insight into the opportunities that integrate complexity concepts into model building offers, we extend the work previously undertaken on the complexity dimension in project management. We do so analyzing the results obtained with classical linear models and artificial neural networks when complexity is considered as another managerial parameter. For that purpose, we have used the International Software Benchmarking Standards Group data set. The results obtained proved the benefits of integrating the complexity of the projects at hand into the models. They also addressed the need of a complex system, such as artificial neural networks, to capture the fine nuances of the complex systems to be modeled, the projects.M. Castejón-Limas · V.

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“…Ref. [7] resolves the uncertain question based on the use of standard deviation of each partition cell. As the splitting method, they all suffer from a serious complexity barrier that greatly limits their practical use.…”

Section: Previous Workmentioning

confidence: 99%

“…Ref. [7] proposed a modified splitting method. They use standard deviation of each Voronoi cell as the splitting condition and then design a VQ codebook with the partitioning space of equally viable and equivalent partitions.…”

Section: Introductionmentioning

confidence: 99%

Fast Vector Quantization Algorithm Based on Vector Features

Shan-xue

2010

2010 International Conference on Electrical and Control Engineering

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The codebook design of vector quantization (VQ) based on a training set is computationally very expensive due to a lot of distance computations in the process of its clustering. In order to speed up the process of VQ codebook design, a fast algorithm is proposed in this paper. The proposed algorithm puts the training vectors into an orderly whole according to the characteristic values of training vectors. An ordered initial codebook is got from the ordered training sets. The clustering process of VQ is speed up by employing the fast kick-out conditions in the ordered codebook. Experimental results confirmed that the proposed method can speed up the design process and improve the codebook performance.

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Optimization of code book in vector quantization

Cited by 14 publications

References 5 publications

Random Amino Acid Mutations and Protein Misfolding Lead to Shannon Limit in Sequence-Structure Communication

Random Amino Acid Mutations and Protein Misfolding Lead to Shannon Limit in Sequence-Structure Communication

Effort estimates through project complexity

Fast Vector Quantization Algorithm Based on Vector Features

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