17,680 research outputs found
Automated reliability assessment for spectroscopic redshift measurements
We present a new approach to automate the spectroscopic redshift reliability
assessment based on machine learning (ML) and characteristics of the redshift
probability density function (PDF).
We propose to rephrase the spectroscopic redshift estimation into a Bayesian
framework, in order to incorporate all sources of information and uncertainties
related to the redshift estimation process, and produce a redshift posterior
PDF that will be the starting-point for ML algorithms to provide an automated
assessment of a redshift reliability.
As a use case, public data from the VIMOS VLT Deep Survey is exploited to
present and test this new methodology. We first tried to reproduce the existing
reliability flags using supervised classification to describe different types
of redshift PDFs, but due to the subjective definition of these flags, soon
opted for a new homogeneous partitioning of the data into distinct clusters via
unsupervised classification. After assessing the accuracy of the new clusters
via resubstitution and test predictions, unlabelled data from preliminary mock
simulations for the Euclid space mission are projected into this mapping to
predict their redshift reliability labels.Comment: Submitted on 02 June 2017 (v1). Revised on 08 September 2017 (v2).
Latest version 28 September 2017 (this version v3
Key point selection and clustering of swimmer coordination through Sparse Fisher-EM
To answer the existence of optimal swimmer learning/teaching strategies, this
work introduces a two-level clustering in order to analyze temporal dynamics of
motor learning in breaststroke swimming. Each level have been performed through
Sparse Fisher-EM, a unsupervised framework which can be applied efficiently on
large and correlated datasets. The induced sparsity selects key points of the
coordination phase without any prior knowledge.Comment: Presented at ECML/PKDD 2013 Workshop on Machine Learning and Data
Mining for Sports Analytics (MLSA2013
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