Restoration of damaged slices in images using matrix pseudo inversion

Ajorloo, Hossein; Shalmani, Mohammad Taghi Manzuri; Lakdashti, Abolfazl

doi:10.1109/iscis.2007.4456843

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…Some non iterative procedures based on the evaluation of generalized pseudoinverse matrices have been recently proposed as novel learning algorithms for SLFNs: among them the method to improve performance of multilayer perceptron by Halawa [2], the extreme learning machine (elm) [3] and some studies more application oriented [4,5,6]. Usually, input weights (linking input and hidden layers) are randomly chosen, and output weights (linking hidden and output layers) are analytically determined by the Moore-Penrose (MP) generalized inverse (or pseudoinverse).…”

Section: Introductionmentioning

confidence: 99%

Matrix Pseudoinversion for Image Neural Processing

Cancelliere

Gai²,

Artières

et al. 2012

Neural Information Processing

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Abstract.Recently some novel strategies have been proposed for training of Single Hidden Layer Feedforward Networks, that set randomly the weights from input to hidden layer, while weights from hidden to output layer are analytically determined by Moore-Penrose generalised inverse. Such non-iterative strategies are appealing since they allow fast learning, but some care may be required to achieve good results, mainly concerning the procedure used for matrix pseudoinversion. This paper proposes a novel approach based on original determination of the initialization interval for input weights, a careful choice of hidden layer activation functions and on critical use of generalised inverse to determine output weights. We show that this key step suffers from numerical problems related to matrix invertibility, and we propose a heuristic procedure for bringing more robustness to the method. We report results on a difficult astronomical image analysis problem of chromaticity diagnosis to illustrate the various points under study.

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Section: Introductionmentioning

confidence: 99%

Matrix Pseudoinversion for Image Neural Processing

Cancelliere

Gai²,

Artières

et al. 2012

Neural Information Processing

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“…Many theorethical and application-oriented studies in the last years were devoted to the use of these single-pass techniques, easy to implement and computationally fast; among the most known we recall the ELM method [21] while others important works are e.g. [20,[22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Real-time detection of driver distraction: random projections for pseudo-inversion-based neural training

et al. 2019

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There is an accumulating evidence that distracted driving is a leading cause of vehicle crashes and accidents. In order to support safe driving, numerous methods of detecting distraction have been proposed, which are empirically focused on certain driving contexts and gaze behavior. This paper aims at illustrating a method for the non-intrusive and real-time detection of visual distraction based on vehicle dynamics data and environmental data, without using eye-tracker information. Experiments are carried out in the context of the automotive domain of the European project Holides, which addresses development and qualification of Adaptive Cooperative Human-Machine Systems, and is co-funded by ARTEMIS Joint Undertaking and Italian University, Educational and Research Department. The collected data are analised by a single layer feedforward neural network trained through pseudo-inversion methods, characterized by direct determination of output weights given randomly set input weights and biases. One main feature of our work is the convenient setting of input weights by the so called random projections: the presence of a great number of null elements in the involved matrices makes especially parsimonious the use at run time of the trained network. Moreover, we use a genetic approach to better explore the input weights network space. The obtained results show both better performance with respect to classical methods and effective and parsimonious use of memory resources.

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“…Many application-oriented studies in the last years established the effectiveness of pseudoinversion-based methods; some are described for example in (Nguyen et al, 2010;Kohno et al, 2010;Ajorloo et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Local search and pseudoinversion: an hybrid approach to neural network training

et al. 2016

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We consider recent successful techniques proposed for neural network training that set randomly the weights from input to hidden layer, while weights from hidden to output layer are analytically determined by Moore-Penrose generalised inverse. This study aims to analyze the impact on performances when the completely random sampling of the space of input weights is replaced by a local search procedure over a discretized set of weights. The performances of the proposed training methods are assessed through computational experience on several UCI datasets.

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Restoration of damaged slices in images using matrix pseudo inversion

Cited by 7 publications

References 21 publications

Matrix Pseudoinversion for Image Neural Processing

Matrix Pseudoinversion for Image Neural Processing

Real-time detection of driver distraction: random projections for pseudo-inversion-based neural training

Local search and pseudoinversion: an hybrid approach to neural network training

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