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Differentiated Data Persistence with Priority Random Linear Codes
, 2006
"... Both peertopeer and sensor networks have the fundamental characteristics of node churn and failures. Peers in P2P networks are highly dynamic, whereas sensors are not dependable. As such, maintaining the persistence of periodically measured data in a scalable fashion has become a critical challeng ..."
Abstract

Cited by 25 (2 self)
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Both peertopeer and sensor networks have the fundamental characteristics of node churn and failures. Peers in P2P networks are highly dynamic, whereas sensors are not dependable. As such, maintaining the persistence of periodically measured data in a scalable fashion has become a critical challenge in such systems, without the use of centralized servers. To better cope with node dynamics and failures, we propose priority random linear codes, as well as their affiliated predistribution protocols, to maintain measurement data in different priorities, such that critical data have a higher opportunity to survive node failures than data of less importance. A salient feature of priority random linear codes is the ability to partially recover more important subsets of the original data with higher priorities, when it is not feasible to recover all of them due to node dynamics. We present extensive analytical and experimental results to show the effectiveness of priority random linear codes. 1
Analyzing the Stochastic Complexity via Tree Polynomials
, 2005
"... Stochastic complexity of a data set is defined as the shortest possible code length for the data obtainable by using some fixed set of models. This measure ..."
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Cited by 7 (5 self)
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Stochastic complexity of a data set is defined as the shortest possible code length for the data obtainable by using some fixed set of models. This measure
A Fast Normalized Maximum Likelihood Algorithm for Multinomial Data
 In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI05
, 2005
"... Stochastic complexity of a data set is defined as the shortest possible code length for the data obtainable by using some fixed set of models. This measure is of great theoretical and practical importance as a tool for tasks such as model selection or data clustering. In the case of multinomial data ..."
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Cited by 5 (3 self)
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Stochastic complexity of a data set is defined as the shortest possible code length for the data obtainable by using some fixed set of models. This measure is of great theoretical and practical importance as a tool for tasks such as model selection or data clustering. In the case of multinomial data, computing the modern version of stochastic complexity, defined as the Normalized Maximum Likelihood (NML) criterion, requires computing a sum with an exponential number of terms. Furthermore, in order to apply NML in practice, one often needs to compute a whole table of these exponential sums. In our previous work, we were able to compute this table by a recursive algorithm. The purpose of this paper is to significantly improve the time complexity of this algorithm. The techniques used here are based on the discrete Fourier transform and the convolution theorem.
Differentiated Data Persistence with Priority Random Linear Codes
"... Both peertopeer and sensor networks have the fundamental characteristics of node churn and failures. Peers in P2P networks are highly dynamic, whereas sensors are not dependable. As such, maintaining the persistence of periodically measured data in a scalable fashion has become a critical challeng ..."
Abstract
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Both peertopeer and sensor networks have the fundamental characteristics of node churn and failures. Peers in P2P networks are highly dynamic, whereas sensors are not dependable. As such, maintaining the persistence of periodically measured data in a scalable fashion has become a critical challenge in such systems, without the use of centralized servers. To better cope with node dynamics and failures, we propose priority random linear codes, as well as their affiliated predistribution protocols, to maintain measurement data in different priorities, such that critical data have a higher opportunity to survive node failures than data of less importance. A salient feature of priority random linear codes is the ability to partially recover more important subsets of the original data with higher priorities, when it is not feasible to recover all of them due to node dynamics. We present extensive analytical and experimental results to show the effectiveness of priority random linear codes. 1
Differentiated Data Persistence with Priority Random Linear Codes
"... Both peertopeer and sensor networks have the fundamental characteristics of node churn and failures. Peers in P2P networks are highly dynamic, whereas sensors are not dependable. As such, maintaining the persistence of periodically measured data in a scalable fashion has become a critical challeng ..."
Abstract
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Both peertopeer and sensor networks have the fundamental characteristics of node churn and failures. Peers in P2P networks are highly dynamic, whereas sensors are not dependable. As such, maintaining the persistence of periodically measured data in a scalable fashion has become a critical challenge in such systems, without the use of centralized servers. To better cope with node dynamics and failures, we propose priority random linear codes, as well as their affiliated predistribution protocols, to maintain measurement data in different priorities, such that critical data have a higher opportunity to survive node failures than data of less importance. A salient feature of priority random linear codes is the ability to partially recover more important subsets of the original data with higher priorities, when it is not feasible to recover all of them due to node dynamics. We present extensive analytical and experimental results to show the effectiveness of priority random linear codes. 1
Minimum Description Length Principle for Maximum Entropy Model Selection
"... Abstract—In maximum entropy method, one chooses a distribution from a set of distributions that maximizes the Shannon entropy for making inference from incomplete information. There are various ways to specify this set of distributions, the important special case being when this set is described by ..."
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Abstract—In maximum entropy method, one chooses a distribution from a set of distributions that maximizes the Shannon entropy for making inference from incomplete information. There are various ways to specify this set of distributions, the important special case being when this set is described by meanvalue constraints of some feature functions. In this case, maximum entropy method fixes an exponential distribution depending on the feature functions that have to be chosen a priori. In this paper, we treat the problem of selecting a maximum entropy model given various feature subsets and their moments, as a model selection problem, and present a minimum description length (MDL) formulation to solve this problem. For this, we derive normalized maximum likelihood (NML) codelength for these models. Furthermore, we show that the minimax entropy method is a special case of maximum entropy model selection, where one assumes that complexity of all the models are equal. We extend our approach to discriminative maximum entropy models. We apply our approach to gene selection problem to select the number of moments for each gene for fixing the model. I.