Unsupervised K-Nearest Neighbor Regression

Details Unsupervised K-Nearest Neighbor Regression Unsupervised K-Nearest Neighbor Regression

2011-07-19

arxiv.org/abs/1107.3600v2

Statistics

Machine Learning

4 pages, 12 figures

Scientific paper

In many scientific disciplines structures in high-dimensional data have to be found, e.g., in stellar spectra, in genome data, or in face recognition tasks. In this work we present a novel approach to non-linear dimensionality reduction. It is based on fitting K-nearest neighbor regression to the unsupervised regression framework for learning of low-dimensional manifolds. Similar to related approaches that are mostly based on kernel methods, unsupervised K-nearest neighbor (UNN) regression optimizes latent variables w.r.t. the data space reconstruction error employing the K-nearest neighbor heuristic. The problem of optimizing latent neighborhoods is difficult to solve, but the UNN formulation allows the design of efficient strategies that iteratively embed latent points to fixed neighborhood topologies. UNN is well appropriate for sorting of high-dimensional data. The iterative variants are analyzed experimentally.

Affiliated with

Also associated with

No associations

Rating

Unsupervised K-Nearest Neighbor Regression 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 Unsupervised K-Nearest Neighbor Regression, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Unsupervised K-Nearest Neighbor Regression will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-513245