Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions

Details Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions

2012-02-14

arxiv.org/abs/1202.3758v1

Computer Science

Learning

Scientific paper

Low-dimensional embedding, manifold learning, clustering, classification, and anomaly detection are among the most important problems in machine learning. The existing methods usually consider the case when each instance has a fixed, finite-dimensional feature representation. Here we consider a different setting. We assume that each instance corresponds to a continuous probability distribution. These distributions are unknown, but we are given some i.i.d. samples from each distribution. Our goal is to estimate the distances between these distributions and use these distances to perform low-dimensional embedding, clustering/classification, or anomaly detection for the distributions. We present estimation algorithms, describe how to apply them for machine learning tasks on distributions, and show empirical results on synthetic data, real word images, and astronomical data sets.

Affiliated with

Also associated with

No associations

Rating

Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions 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 Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-90663