1,404 research outputs found
Estimating Extinction using Unsupervised Machine Learning
Dust extinction is the most robust tracer of the gas distribution in the
interstellar medium, but measuring extinction is limited by the systematic
uncertainties involved in estimating the intrinsic colors to background stars.
In this paper we present a new technique, PNICER, that estimates intrinsic
colors and extinction for individual stars using unsupervised machine learning
algorithms. This new method aims to be free from any priors with respect to the
column density and intrinsic color distribution. It is applicable to any
combination of parameters and works in arbitrary numbers of dimensions.
Furthermore, it is not restricted to color space. Extinction towards single
sources is determined by fitting Gaussian Mixture Models along the extinction
vector to (extinction-free) control field observations. In this way it becomes
possible to describe the extinction for observed sources with probability
densities. PNICER effectively eliminates known biases found in similar methods
and outperforms them in cases of deep observational data where the number of
background galaxies is significant, or when a large number of parameters is
used to break degeneracies in the intrinsic color distributions. This new
method remains computationally competitive, making it possible to correctly
de-redden millions of sources within a matter of seconds. With the
ever-increasing number of large-scale high-sensitivity imaging surveys, PNICER
offers a fast and reliable way to efficiently calculate extinction for
arbitrary parameter combinations without prior information on source
characteristics. PNICER also offers access to the well-established NICER
technique in a simple unified interface and is capable of building extinction
maps including the NICEST correction for cloud substructure. PNICER is offered
to the community as an open-source software solution and is entirely written in
Python.Comment: Accepted for publication in A&A, source code available at
http://smeingast.github.io/PNICER
Marginal likelihoods of distances and extinctions to stars: computation and compact representation
We present a method for obtaining the likelihood function of distance and
extinction to a star given its photometry. The other properties of the star
(its mass, age, metallicity and so on) are marginalised assuming a simple
Galaxy model. We demonstrate that the resulting marginalised likelihood
function can be described faithfully and compactly using a Gaussian mixture
model. For dust mapping applications we strongly advocate using monochromatic
over bandpass extinctions, and provide tables for converting from the former to
the latter for different stellar types.Comment: 14 pages, 12 figures. Accepted for publication in MNRAS. Source code
is available at https://github.com/stuartsal
Approximated and User Steerable tSNE for Progressive Visual Analytics
Progressive Visual Analytics aims at improving the interactivity in existing
analytics techniques by means of visualization as well as interaction with
intermediate results. One key method for data analysis is dimensionality
reduction, for example, to produce 2D embeddings that can be visualized and
analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a
well-suited technique for the visualization of several high-dimensional data.
tSNE can create meaningful intermediate results but suffers from a slow
initialization that constrains its application in Progressive Visual Analytics.
We introduce a controllable tSNE approximation (A-tSNE), which trades off speed
and accuracy, to enable interactive data exploration. We offer real-time
visualization techniques, including a density-based solution and a Magic Lens
to inspect the degree of approximation. With this feedback, the user can decide
on local refinements and steer the approximation level during the analysis. We
demonstrate our technique with several datasets, in a real-world research
scenario and for the real-time analysis of high-dimensional streams to
illustrate its effectiveness for interactive data analysis
Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization
Camera relocalization plays a vital role in many robotics and computer vision
tasks, such as global localization, recovery from tracking failure and loop
closure detection. Recent random forests based methods exploit randomly sampled
pixel comparison features to predict 3D world locations for 2D image locations
to guide the camera pose optimization. However, these image features are only
sampled randomly in the images, without considering the spatial structures or
geometric information, leading to large errors or failure cases with the
existence of poorly textured areas or in motion blur. Line segment features are
more robust in these environments. In this work, we propose to jointly exploit
points and lines within the framework of uncertainty driven regression forests.
The proposed approach is thoroughly evaluated on three publicly available
datasets against several strong state-of-the-art baselines in terms of several
different error metrics. Experimental results prove the efficacy of our method,
showing superior or on-par state-of-the-art performance.Comment: published as a conference paper at 2018 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS
A Dynamic Parameter Tuning Algorithm For Rbf Neural Networks
The objective of this thesis is to present a methodology for fine-tuning the parameters of radial basis function (RBF) neural networks, thus improving their performance. Three main parameters affect the performance of an RBF network. They are the centers and widths of the RBF nodes and the weights associated with each node. A gridded center and orthogonal search algorithm have been used to initially determine the parameters of the RBF network. A parameter tuning algorithm has been developed to optimize these parameters and improve the performance of the RBF network. When necessary, the recursive least square solution may be used to include new nodes to the network architecture. To study the behavior of the proposed network, six months of real data at fifteen-minute intervals has been collected from a North American pulp and paper company. The data has been used to evaluate the performance of the proposed network in the approximation of the relationship between the optical properties of base sheet paper and the process variables. The experiments have been very successful and Pearson correlation coefficients of up to 0.98 have been obtained for the approximation. The objective of this thesis is to present a methodology for fine-tuning the parameters of radial basis function (RBF) neural networks, thus improving their performance. Three main parameters affect the performance of an RBF network. They are the centers and widths of the RBF nodes and the weights associated with each node. A gridded center and orthogonal search algorithm have been used to initially determine the parameters of the RBF network. A parameter tuning algorithm has been developed to optimize these parameters and improve the performance of the RBF network. When necessary, the recursive least square solution may be used to include new nodes to the network architecture. To study the behavior of the proposed network, six months of real data at fifteen-minute intervals has been collected from a North American pulp and paper company. The data has been used to evaluate the performance of the proposed network in the approximation of the relationship between the optical properties of base sheet paper and the process variables. The experiments have been very successful and Pearson correlation coefficients of up to 0.98 have been obtained for the approximation. The objective of this thesis is to present a methodology for fine-tuning the parameters of radial basis function (RBF) neural networks, thus improving their performance. Three main parameters affect the performance of an RBF network. They are the centers and widths of the RBF nodes and the weights associated with each node. A gridded center and orthogonal search algorithm have been used to initially determine the parameters of the RBF network. A parameter tuning algorithm has been developed to optimize these parameters and improve the performance of the RBF network. When necessary, the recursive least square solution may be used to include new nodes to the network architecture. To study the behavior of the proposed network, six months of real data at fifteen-minute intervals has been collected from a North American pulp and paper company. The data has been used to evaluate the performance of the proposed network in the approximation of the relationship between the optical properties of base sheet paper and the process variables. The experiments have been very successful and Pearson correlation coefficients of up to 0.98 have been obtained for the approximation
Gradient-based training and pruning of radial basis function networks with an application in materials physics
Many applications, especially in physics and other sciences, call for easily interpretable and robust machine learning techniques. We propose a fully gradient-based technique for training radial basis function networks with an efficient and scalable open-source implementation. We derive novel closed form optimization criteria for pruning the models for continuous as well as binary data which arise in a challenging real-world material physics problem. The pruned models are optimized to provide compact and interpretable versions of larger models based on informed assumptions about the data distribution. Visualizations of the pruned models provide insight into the atomic configurations that determine atom-level migration processes in solid matter; these results may inform future research on designing more suitable descriptors for use with machine learning algorithms. (c) 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe
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