Projection Pursuit Regression

Klinke, Sigbert; Grassmann, Janet

doi:10.1002/9781118150658.ch16

Cited by 4 publications

(2 citation statements)

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“…The auto-associative neural networks can also be view as a non-linear PCA model [2,27,4,19]. In [13] we propose the auto-associative models (AAM) as candidates to the generalization of PCA using a projection pursuit regression algorithm [9,25] adapted to the auto-associative case. A common point of these approaches is that they have the intent to estimate an auto-associative model whose definition is given hereafter.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic auto-associative models and semi-linear PCA

Iovleff

2014

Adv Data Anal Classif

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Auto-Associative models cover a large class of methods used in data analysis. In this paper, we describe the generals properties of these models when the projection component is linear and we propose and test an easy to implement Probabilistic Semi-Linear Auto-Associative model in a Gaussian setting. We show it is a generalization of the PCA model to the semi-linear case. Numerical experiments on simulated datasets and a real astronomical application highlight the interest of this approach.

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Section: Introductionmentioning

confidence: 99%

Probabilistic auto-associative models and semi-linear PCA

Iovleff

2014

Adv Data Anal Classif

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“…Firstly, the step over-complete dictionary [23] which can effectively reduce the operation time is constructed according to the direction finding need and the array flow pattern of the sensors. Then the matching pursuit (MP) algorithm [24][25][26] is used according to the step-by-step over-complete dictionary to search for the optimal DOA estimation. Moreover, using the spatial direction finding angle, the spatial position of the PD source is determined by using the three array platforms location method.…”

Section: Introductionmentioning

confidence: 99%

Research on Partial Discharge Source Localization Based on an Ultrasonic Array and a Step-by-Step Over-Complete Dictionary

et al. 2017

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Partial discharge (PD) in electrical equipment is one of the major causes of electrical insulation failures. Fast and accurate positioning of PD sources allows timely elimination of insulation faults. In order to improve the accuracy of PD detection, this paper mainly studies the direction of arrival (DOA) estimation of PD ultrasonic signals based on a step-by-step over-complete dictionary. The simulation results show that the step by step dictionary can improve the operation speed and save signal processing time. Firstly, a step-by-step over-complete dictionary covering all the angles of space is established according to the expression of the steering vector for a matching pursuit direction finding algorithm, which can save computation time. Then, the step-by-step complete dictionary is set up according to the direction vector, and the atomic precision is respectively set to 10 • , 1 • and 0.1 • . The matching pursuit algorithm is used to carry out the sparse representation of the received data X and select the optimal atom from the step-by-step complete dictionary, and the angle information contained in atoms is DOA of the PD sources. According to the direction finding results, combined with the installation location of the ultrasonic array sensor, the spatial position of a partial discharge source can be obtained using the three platform array location method. Finally, a square ultrasonic array sensor is developed, and an experimental platform for the ultrasonic array detection of partial discharges is set up and used to carry out an experimental study. The results show that the DOA estimation method based on a step-by-step over-complete dictionary can improve the direction finding precision, thereby increasing the subsequent positioning accuracy, and the spatial position estimation error of the PD source obtained under laboratory conditions is about 5 cm, making this a feasible method.

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2012

Computational Statistics

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Projection Pursuit Regression

Cited by 4 publications

References 50 publications

Probabilistic auto-associative models and semi-linear PCA

Probabilistic auto-associative models and semi-linear PCA

Research on Partial Discharge Source Localization Based on an Ultrasonic Array and a Step-by-Step Over-Complete Dictionary

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