Paper Info
Title
Maximum Likelihood Quantization of Genomic Features Using Dynamic Programming
Abstract
Dynamic programming is introduced to quantize a continuous random variable into a discrete random variable. Quantization is often useful before statistical analysis or reconstruction of large network models among multiple random variables. The quantization, through dynamic programming, finds the optimal discrete representation of the original probability density function of a random variable by maximizing the likelihood for the observed data. This algorithm is highly applicable to study genomic features such as the recombination rate across the chromosomes and the statistical properties of non-coding elements such as LINE1. In particular, the recombination rate obtained by quantization is studied for LINE1 elements that are grouped also using quantization by length. The exact and densitypreserving quantization approach provides an alternative superior to the inexact and distance-based k-means clustering algorithm for discretization of a single variable.
Year
DOI
Venue
2007
10.1109/ICMLA.2007.72
ICMLA
Keywords
Field
DocType
quantization approach,discrete random variable,recombination rate,line1 element,random variable,maximum likelihood quantization,optimal discrete representation,continuous random variable,single variable,multiple random variable,dynamic programming,probability,random processes,statistical analysis,maximum likelihood,probability density function,maximum likelihood estimation,network model,k means clustering
Discretization,Random variable,Pattern recognition,Linde–Buzo–Gray algorithm,Computer science,Stochastic process,Vector quantization,Artificial intelligence,Quantization (signal processing),Cluster analysis,Probability density function,Machine learning
Conference
ISBN
Citations 
PageRank 
0-7695-3069-9
0
0.34
References 
Authors
12
3
Name
Order
Citations
PageRank
Mingzhou (Joe) Song1152.27
Robert M. Haralick2102622605.93
Stephane Boissinot301.01