6,440 research outputs found
k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)
Perhaps the most straightforward classifier in the arsenal or machine
learning techniques is the Nearest Neighbour Classifier -- classification is
achieved by identifying the nearest neighbours to a query example and using
those neighbours to determine the class of the query. This approach to
classification is of particular importance because issues of poor run-time
performance is not such a problem these days with the computational power that
is available. This paper presents an overview of techniques for Nearest
Neighbour classification focusing on; mechanisms for assessing similarity
(distance), computational issues in identifying nearest neighbours and
mechanisms for reducing the dimension of the data.
This paper is the second edition of a paper previously published as a
technical report. Sections on similarity measures for time-series, retrieval
speed-up and intrinsic dimensionality have been added. An Appendix is included
providing access to Python code for the key methods.Comment: 22 pages, 15 figures: An updated edition of an older tutorial on kN
Preprocessing Solar Images while Preserving their Latent Structure
Telescopes such as the Atmospheric Imaging Assembly aboard the Solar Dynamics
Observatory, a NASA satellite, collect massive streams of high resolution
images of the Sun through multiple wavelength filters. Reconstructing
pixel-by-pixel thermal properties based on these images can be framed as an
ill-posed inverse problem with Poisson noise, but this reconstruction is
computationally expensive and there is disagreement among researchers about
what regularization or prior assumptions are most appropriate. This article
presents an image segmentation framework for preprocessing such images in order
to reduce the data volume while preserving as much thermal information as
possible for later downstream analyses. The resulting segmented images reflect
thermal properties but do not depend on solving the ill-posed inverse problem.
This allows users to avoid the Poisson inverse problem altogether or to tackle
it on each of 10 segments rather than on each of 10 pixels,
reducing computing time by a factor of 10. We employ a parametric
class of dissimilarities that can be expressed as cosine dissimilarity
functions or Hellinger distances between nonlinearly transformed vectors of
multi-passband observations in each pixel. We develop a decision theoretic
framework for choosing the dissimilarity that minimizes the expected loss that
arises when estimating identifiable thermal properties based on segmented
images rather than on a pixel-by-pixel basis. We also examine the efficacy of
different dissimilarities for recovering clusters in the underlying thermal
properties. The expected losses are computed under scientifically motivated
prior distributions. Two simulation studies guide our choices of dissimilarity
function. We illustrate our method by segmenting images of a coronal hole
observed on 26 February 2015
Information Theory and Machine Learning
The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges. This Special Issue, "Machine Learning and Information Theory", aims to collect recent results in this direction reflecting a diverse spectrum of visions and efforts to extend conventional theories and develop analysis tools for these complex machine learning systems
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