Robust Nonlinear Regression: A Greedy Approach Employing Kernels With Application to Image Denoising.

Instead, we employ sparse modeling arguments to explicitly model and estimate the outliers, adopting a greedy approach. ... The proposed robust scheme, i.e., Kernel Greedy Algorithm for Robust Denoising (KGARD), is inspired by the classical Orthogonal Matching Pursuit (OMP) algorithm. ... This is accomplished by replacing the matrix [K 1] with the matrix [K 1 I N ]. 4 Kernel Greedy Algorithm for Robust Denoising (KGARD) Motivation and Proposed Scheme Our proposed scheme, alternates between ...

doi:10.1109/tsp.2017.2708029 fatcat:lpbee7vj5vd2nlnnu3e7chogl4

Multiple Versions

Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). ... Pixels are assumed to be mixtures of a few materials, called endmembers. ... The authors also acknowledge the Army Geospatial Center, US Army Corps of Engineers, for making the HYDICE Rterrain data set available to the community. ...

arXiv:1202.6294v2 fatcat:4vxq62jxvzfynpb75wvvhw4phq

Multiple Versions

Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). ... Pixels are assumed to be mixtures of a few materials, called endmembers. ... The authors also acknowledge the Army Geospatial Center, US Army Corps of Engineers, for making the HYDICE Rterrain data set available to the community. ...

doi:10.1109/jstars.2012.2194696 fatcat:s66a35xjd5dqzkw5wwihq6ux64

DOAJ

This paper aims to investigate the state-of-the-art on existing methodologies that combine DL and FLS, namely deep fuzzy systems, to address regression problems, configuring a topic that is currently not ... Fuzzy logic systems (FLS) are inherently interpretable models, well known in the literature, capable of using nonlinear representations for complex systems through linguistic terms with membership degrees ... Júnior is supported by Fundação para a Ciência e a Tecnologia (FCT) under the grant ref. 2021.04917.BD. ...

arXiv:2209.04230v1 fatcat:mvwyqrhb3nerndnedlq4ccidku

Open Access

To our best knowledge, this is the first work that combines sparse regression models with deep neural network. ... Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. ... As such, the investigators within the ADNI contributed to the ...

doi:10.1016/j.media.2017.01.008 pmid:28167394 pmcid:PMC5808465 fatcat:j56bwaxjrvd5tg6weciyw2dng4

In this paper, we propose a principled algorithm within the maximum a posterior framework to tackle image restoration with a partially known or inaccurate degradation model. ... Rain streak removal and image deconvolution with inaccurate blur kernels are two representative examples of such tasks. ... For example, A can be an identity matrix for denoising, a blur kernel convolution for deconvolution, and a downsampling operator for super-resolution, to name a few. ...

arXiv:1804.04522v4 fatcat:egsutdpnlfc3fcymzxywkd2iny

Multiple Versions

In this paper, we propose a simple but effective coupled neural network, called Deeply Coupled Autoencoder Networks (DCAN), which seeks to build two deep neural networks, coupled with each other in every ... In DCAN, each deep structure is developed via stacking multiple discriminative coupled auto-encoders, a denoising auto-encoder trained with maximum margin criterion consisting of intra-class compactness ... Training a deep network with coupled nonlinear transforms can be achieved by the canonical greedy layer-wise approach [12, 6] . ...

arXiv:1402.2031v1 fatcat:qx7kyxmtsnda3kevmszmwoki6m

Open Access

We propose to use a pre-training technique for a shallow, i.e. with fewer hidden layers, Artificial Neural Network (ANN). ... While this method is usually employed to prevent over-fitting in deep ANNs, our results indicate that even in a shallow network, it improves the accuracy by escaping from local minima. ... As in (1), a nonlinear function s followed by an affine function (with weight w and bias b) are applied on x for creating a new representation of data. = ( ) = ( + ) (1) After that, by using a nonlinear ...

doi:10.1109/iccke.2017.8167880 fatcat:cusd4fkhsbgobmbdyblq6vjhpy

To cope with this challenge, this paper advocates a convex duality framework that makes a two-layer fully-convolutional ReLU denoising network amenable to convex optimization. ... The non-convex and opaque nature of neural networks, however, hinders their utility in sensitive applications such as medical imaging. ... INTRODUCTION In the age of AI, image reconstruction has witnessed a paradigm shift that impacts several applications ranging from natural image super-resolution to medical imaging. ...

arXiv:2012.05169v1 fatcat:dnmyn3hfofa7dchjalzm6a56oy

Open Access

Furthermore, after being trained, our model can be easily transferred to new likelihood terms to solve untrained tasks, or be combined with existing priors to further improve image restoration quality. ... The method requires a single-pass training and allows for reuse across various problems and conditions while achieving an efficiency comparable to previous discriminative approaches. ... DTL outperforms kernel regression (KR) method with either classic or steering kernels, NLR-CS, GSR and EPLL in both PSNR and SSIM. ...

doi:10.1109/tip.2018.2831925 pmid:29993740 fatcat:prsa74c75jhyhnufmzbkykhqza

Multiple Versions

Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. ... Importantly, valuable industrial equipment needs continuous monitoring to enhance the safety, reliability, and availability and to decrease the cost of maintenance of modern industrial systems and applications ... Kernel-nonlinear SVM along with Gaussian radial basis function is employed. ...

doi:10.1155/2020/8843759 fatcat:h4zyvhct6nb7lpsj7j5f3yror4

DOAJ

This review article provides a comprehensive survey on the application of DL, RL, and Deep RL techniques in mining Biological data. ... In addition, we compare performances of DL techniques when applied to different datasets across various application domains. ... Acknowledgment The authors would like to thank Dr. Pawel Raif and Dr. Kamal Abu-Hassan for useful discussions during the early stage of the work. ...

doi:10.1109/tnnls.2018.2790388 pmid:29771663 fatcat:6r63zihrfvea7cto4ei3mlvqtu

Multiple Versions

In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. ... Addressing such channels is of paramount importance, both in itself, since high-altitude imaging, environmental conditions, and imaging systems trade-offs lead to low-quality observation, as well as to ... To achieve this objective, a 3D CNN, i.e., a CNN with three-dimensional convolutional kernels, is employed. ...

doi:10.3390/s19183929 pmid:31547250 pmcid:PMC6767260 fatcat:fp7lezjwcfg5fol5hxmgoejg7a

DOAJ

Aim: We hope to provide a general data analysis pipeline of mesoscale neural imaging shared between imaging modalities and systems. Approach: We divide the pipeline into two main stages. ... We explain the principles of these procedures and compare different approaches and their application scopes with detailed discussions about the shortcomings and remaining challenges. ... The regression modal could be expressed as y ¼ Xβ, 108 where β is a concatenated event kernel vector to be regressed. ...

doi:10.1117/1.nph.9.4.041407 pmid:35450225 pmcid:PMC9010663 fatcat:yalq65vasrgzbf3gwweu5vkrcu

DOAJ

Deep learning approaches have also been found to be suitable for big data analysis with successful applications to computer vision, pattern recognition, speech recognition, natural language processing, ... It trains the ANNs with a teacher-based supervised learning approach. ... In the CRBMs, a convolution is computed with a normal RBM as the kernel. ...

doi:10.1016/j.neucom.2016.12.038 fatcat:nkxvbhp47rfflpi5jev7hk4yq4

Robust Nonlinear Regression: A Greedy Approach Employing Kernels With Application to Image Denoising

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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches [article]

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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

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Survey on Deep Fuzzy Systems in regression applications: a view on interpretability [article]

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Deep ensemble learning of sparse regression models for brain disease diagnosis

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Simultaneous Fidelity and Regularization Learning for Image Restoration [article]

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Deeply Coupled Auto-encoder Networks for Cross-view Classification [article]

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Pre-training of an artificial neural network for software fault prediction

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Convex Regularization Behind Neural Reconstruction [article]

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Discriminative Transfer Learning for General Image Restoration

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A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor

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Applications of Deep Learning and Reinforcement Learning to Biological Data

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Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

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Review on data analysis methods for mesoscale neural imaging in vivo

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A survey of deep neural network architectures and their applications

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