4.5 Article

Automatic dimensionality selection from the scree plot via the use of profile likelihood

Journal

COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 51, Issue 2, Pages 918-930

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.csda.2005.09.010

Keywords

data compression; denoising; isomap; latent semantic indexing; manifold learning; principal component analysis (PCA); resampling methods; singular value decomposition (SVD)

Ask authors/readers for more resources

Most dimension reduction techniques produce ordered coordinates so that only the first few coordinates need be considered in subsequent analyses. The choice of how many coordinates to use is often made with a visual heuristic, i.e., by making a scree plot and looking for a big gap or an elbow. In this article, we present a simple and automatic procedure to accomplish this goal by maximizing a simple profile likelihood function. We give a wide variety of both simulated and real examples. (c) 2005 Elsevier B.V. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available