Novelty detection and neural network validation

Bishop, Chris

doi:10.1049/ip-vis:19941330

Cited by 369 publications

(105 citation statements)

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“…The variance was determined using the nearest-neighbour method proposed by Bishop [2], in which the average of the squared Euclidean distance to the set of 10 nearest neighbours is determined for each point in ,and is estimated by calculating the average over all points:…”

Section: Methodsmentioning

confidence: 99%

“…An alternative approach to detecting patient deterioration from changes in vital signs is that of novelty detection [2, 20], or one-class classification, which involves the construction of a multivariate, multimodal model of normality using examples of “normal” vital signs. This then allows the classification of test data as either “normal” or “abnormal” with respect to that model.…”

Section: Introductionmentioning

confidence: 99%

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Modelling physiological deterioration in post-operative patient vital-sign data

Pimentel

Clifton

et al. 2013

Med Biol Eng Comput

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Patients who undergo upper-gastrointestinal surgery have a high incidence of post-operative complications, often requiring admission to the intensive care unit several days after surgery. A dataset comprising observational vital-sign data from 171 post-operative patients taking part in a two-phase clinical trial at the Oxford Cancer Centre, was used to explore the trajectory of patients’ vital-sign changes during their stay in the post-operative ward using both univariate and multivariate analyses. A model of normality based vital-sign data from patients who had a “normal” recovery was constructed using a kernel density estimate, and tested with “abnormal” data from patients who deteriorated sufficiently to be re-admitted to the intensive care unit. The vital-sign distributions from “normal” patients were found to vary over time from admission to the post-operative ward to their discharge home, but no significant changes in their distributions were observed from halfway through their stay on the ward to the time of discharge. The model of normality identified patient deterioration when tested with unseen “abnormal” data, suggesting that such techniques may be used to provide early warning of adverse physiological events.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling physiological deterioration in post-operative patient vital-sign data

Pimentel

Clifton

et al. 2013

Med Biol Eng Comput

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“…For each sample, a check is then made that the probability density of the sample is higher than a lower limit for the density in the training set. This is similar to the novelty detection mechanism in Bishop (1994). If the probability density of the sample is too low, that point is rejected.…”

Section: Methodsmentioning

confidence: 87%

A framework for the investigation of multiparametric dependences applied to total radiated power of JET plasmas

Svensson

Mohanti

Lawson

et al. 2001

Plasma Phys. Control. Fusion

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A framework is developed for investigating complex multivariate relationships in a dataset. This is based on using the universal approximation abilities of a multi-layer perceptron (MLP) neural network to predict a quantity of interest from a large set of parameters. A measure of redundancy is derived, and used in such a way that the average influence on the predicted quantity from any parameter can be estimated. Input parameters can be ordered in terms of increasing redundancy and therefore assist in finding the most important parameters a phenomenon of interest depends upon. In spite of the problem being multi-dimensional, the functional form of the one-to-one relationship between a parameter and a quantity of interest can be visualized. This framework is then used together with sensitivity analysis to investigate the dependence of the total radiated power of JET plasmas on a large number of parameters, leading to the identification of a much smaller set of parameters to be used in an effective MLP predictor of total radiated power.

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“…A novelty detection scheme Bishop, 1994) was used to verify the algorithm applicability range by evaluating the representativeness of the input data in the training dataset (D'Alimonte et al, 2003;Mélin et al, 2011;Sá et al, 2015). The adopted applicability range is based on a novelty index (η) presented in published works (D'Alimonte et al, 2013;Sá et al, 2015).…”

Section: Regional Mlp Nn Algorithmmentioning

confidence: 99%

Technical note: Algal Pigment Index 2 in the Atlantic off the southwest Iberian Peninsula: standard and regional algorithms

et al. 2016

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Abstract. In this study, Algal Pigment Index 2 (API2) is investigated in Sagres, an area located in the Atlantic off the southwestern Iberian Peninsula. Standard results provided by the MEdium Resolution Image Spectrometer (MERIS) ocean colour sensor were compared with alternative data products, determined through a regional inversion scheme, using both MERIS and in situ remote sensing reflectances (R rs ) as input data. The reference quantity for performance assessment is in situ total chlorophyll a (TChl a) concentration estimated through a phytoplankton absorption coefficient (i.e. equivalent to API2). Additional comparison of data products has also been addressed for TChl a concentration determined by high-performance liquid chromatography. The MERIS matchup analysis revealed a systematic underestimation of TChl a, which was confirmed with an independent comparison of product map analysis. The study demonstrates the importance of regional algorithms for the study area that could complement upcoming standard results of the current Sentinel-3/OLCI space mission. IntroductionThe MEdium Resolution Image Spectrometer (MERIS) space sensor, operated by the European Space Agency (ESA) on-board the ENVISAT platform from 2002 to 2012, has been continuously supported by investigations for the assessment and improvement of data products. Commissioned studies include the validation of radiometric data such as the R rs Kajiyama et al., 2014), as well as the analyses of derived product maps D'Alimonte et al., 2014;Cristina et al., 2016b). These MERIS validation activities have established an important basis to address Earth observation (EO) capabilities through the Ocean Land Colour Instrument (OLCI) sensor launched on the Sentinel-3 satellite in February 2016. OLCI data products are the main component of the Copernicus European programme to monitor the marine environment, and the retrieval of chlorophyll a (Chl a) is a core task of the Sentinel-3 space mission. Chl a is needed to estimate the phytoplankton biomass in the ocean and to contribute to a variety of interrelated investigations and applications, including climate data records, environmental legislation, and a number of economic activities such as fisheries and aquaculture. After the removal of the atmospheric contribution to the signal recorded at the top of the atmosphere, Chl a can be estimated from the bottom-of-atmosphere (BOA) R rs values, using the standard approach with polynomial algorithms based on band ratios of the input radiometric quantities. The corresponding MERIS data product is denoted Algal Pigment Index 1 (API1) (Morel and Antoine, 2011). The use of band ratio is based on the assumption that seawater optical properties are driven by Chl a. A tendency towards overestimation has, however, been documented in optically complex marine conditions . This can occur when optically active constituents, such as coloured dissolved orPublished by Copernicus Publications on behalf of the European Geosciences Union.

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Novelty detection and neural network validation

Cited by 369 publications

References 0 publications

Modelling physiological deterioration in post-operative patient vital-sign data

Modelling physiological deterioration in post-operative patient vital-sign data

A framework for the investigation of multiparametric dependences applied to total radiated power of JET plasmas

Technical note: Algal Pigment Index 2 in the Atlantic off the southwest Iberian Peninsula: standard and regional algorithms

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