Approximate Computation and Implicit Regularization for Very Large-scale Data Analysis

Details Approximate Computation and Implicit Regularization for Very Large-scale Data Analysis Approximate Computation and Implicit Regularization for Very Large-scale Data Analysis

2012-03-04

arxiv.org/abs/1203.0786v1

Computer Science

Data Structures and Algorithms

To appear in the Proceedings of the 2012 ACM Symposium on Principles of Database Systems (PODS 2012)

Scientific paper

Database theory and database practice are typically the domain of computer scientists who adopt what may be termed an algorithmic perspective on their data. This perspective is very different than the more statistical perspective adopted by statisticians, scientific computers, machine learners, and other who work on what may be broadly termed statistical data analysis. In this article, I will address fundamental aspects of this algorithmic-statistical disconnect, with an eye to bridging the gap between these two very different approaches. A concept that lies at the heart of this disconnect is that of statistical regularization, a notion that has to do with how robust is the output of an algorithm to the noise properties of the input data. Although it is nearly completely absent from computer science, which historically has taken the input data as given and modeled algorithms discretely, regularization in one form or another is central to nearly every application domain that applies algorithms to noisy data. By using several case studies, I will illustrate, both theoretically and empirically, the nonobvious fact that approximate computation, in and of itself, can implicitly lead to statistical regularization. This and other recent work suggests that, by exploiting in a more principled way the statistical properties implicit in worst-case algorithms, one can in many cases satisfy the bicriteria of having algorithms that are scalable to very large-scale databases and that also have good inferential or predictive properties.

Affiliated with

Also associated with

No associations

Rating

Approximate Computation and Implicit Regularization for Very Large-scale Data Analysis does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Approximate Computation and Implicit Regularization for Very Large-scale Data Analysis, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Approximate Computation and Implicit Regularization for Very Large-scale Data Analysis will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-132260