Algorithms 2012, 5(1), 98-112; doi:10.3390/a5010098
Successive Standardization of Rectangular Arrays
1
Department of Health Research and Policy—Biostatistics, HRP Redwood Building, Stanford University School of Medicine, Stanford, CA 94305-5405, USA
2
Department of Electrical Engineering, Stanford University, Packard Electrical Engineering Building, 350 Serra Mall, Stanford, CA 94305, USA
3
Department of Statistics, Stanford University, Sequoia Hall, 390 Serra Mall, Stanford, CA 94305-4065, USA
4
Department of Environmental Earth System Science, Yang and Yamazaki Environment & Energy Building, 473 Via Ortega, Suite 140 Stanford, CA 94305, USA
5
The Woods Institute for the Environment, Jerry Yang & Akiko Yamazaki Environment & Energy Building—MC 4205, 473 Via Ortega, Stanford, CA 94305, USA
*
Authors to whom correspondence should be addressed.
Received: 17 November 2011 / Revised: 20 February 2012 / Accepted: 20 February 2012 / Published: 29 February 2012
(This article belongs to the Special Issue Data Compression, Communication and Processing)
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Abstract
In this note we illustrate and develop further with mathematics and examples, the work on successive standardization (or normalization) that is studied earlier by the same authors in [1] and [2]. Thus, we deal with successive iterations applied to rectangular arrays of numbers, where to avoid technical difficulties an array has at least three rows and at least three columns. Without loss, an iteration begins with operations on columns: first subtract the mean of each column; then divide by its standard deviation. The iteration continues with the same two operations done successively for rows. These four operations applied in sequence completes one iteration. One then iterates again, and again, and again, ... In [1] it was argued that if arrays are made up of real numbers, then the set for which convergence of these successive iterations fails has Lebesgue measure 0. The limiting array has row and column means 0, row and column standard deviations 1. A basic result on convergence given in [1] is true, though the argument in [1] is faulty. The result is stated in the form of a theorem here, and the argument for the theorem is correct. Moreover, many graphics given in [1] suggest that except for a set of entries of any array with Lebesgue measure 0, convergence is very rapid, eventually exponentially fast in the number of iterations. Because we learned this set of rules from Bradley Efron, we call it “Efron’s algorithm”. More importantly, the rapidity of convergence is illustrated by numerical examples.
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MDPI and ACS Style
Olshen, R.A.; Rajaratnam, B. Successive Standardization of Rectangular Arrays. Algorithms 2012, 5, 98-112.
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