Quentin F. Stout

Computer Science and Engineering, University of Michigan

**Abstract:**
Step functions are a fundamental form of approximation, arising in variable width histograms,
databases, segmentation,
approximating sets of planar points, piecewise constant approximations, etc.
We give an algorithm for determining an optimal step function approximation of weighted data, where the error is measured with respect to the weighted L_{∞} norm.
The algorithm takes Θ(n + log n ⋅ b(1+ log n/b)) time and Θ(n) space, where b is the number of steps.
Thus the time is Θ(n log n) in the worst case and Θ(n) when b = O(n/log n loglog n).
The fastest previous algorithms took Θ(n log n) time, independent of b, but required parametric sort, which is highly impractical.
This algorithm is far more practical, is faster when b = o(n), and takes only Θ(n) space.
It is even faster than the best previous algorithm for unweighted data.

With only a minor change the algorithm determines an optimal reduced isotonic regression in the same time and space bounds.
A function is isotonic iff it is nondecreasing, and a function is an * optimal L _{∞} b-step reduced isotonic regression* iff it minimizes the weighted L

The algorithm also solves the k-center problem for 1-dimensional data.
Given a set of weighted values V=(y_{i},w_{i}), i=1 ... n, where the y values are arbitrary real numbers and the w values, the weights, are nonnegative real numbers, and given a set of real numbers S, the weighted L_{∞} distance from V to S is

The paper introduces the interval tree of bounded error envelopes, instead of the full upward and downward error envelopes used previously. This allows a reduction in time and space. The algorithm uses an indirect approach, finding the minimal possible error and using the error to determine the step function. Exploiting the geometric properties of the entries being evaluated in search in an ordered matrix allows a reduction in the time required to find the optimal error.

**Keywords:** stepwise approximation, weighted L_{∞} error, variable width histogram, piecewise constant approximation, reduced isotonic regression, k-center, nonparametric

**Complete paper**, arXiv 1412.2379

**Related work:**
Here is a paper on algorithms for L_{2} reduced isotonic regression, and here is information about the fastest
isotonic regression algorithms, with no constraint on the number of steps,
for a variety of orderings and metrics.

Copyright © 2014-2017 Quentin F. Stout. |