The on-line curvilinear component analysis (onCCA) for real-time data reduction

Cirrincione, Giansalvo; Hérault, Jeanny; Randazzo, Vincenzo

doi:10.1109/ijcnn.2015.7280318

Cited by 18 publications

(13 citation statements)

References 6 publications

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“…Finally, to address the issues regarding the curse of dimensionality, effective Dimensionality Reduction (DR) techniques, which allow also for online performance, could be required [111]. The Growing Curvilinear Component Analysis (GCCA) [113] enables a non-linear distance preserving reduction technique [112], by means of a self-organized incremental neural network architecture, leading to applications in life sciences [39].…”

Section: Discussionmentioning

confidence: 99%

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Rundo

Tangherloni

Nobile

et al. 2019

Expert Systems with Applications

101

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Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram

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Section: Discussionmentioning

confidence: 99%

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Rundo

Tangherloni

Nobile

et al. 2019

Expert Systems with Applications

101

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“…the data projection, is determined as in CCA. On the contrary, if the data fails the novelty test, the firstwinner and its neighbors adapt their weight vectors in the X space by means of the Soft Competitive Learning (SCL [33,34]); as in the previous case, their projections are updated as in CCA.…”

Section: The Growing Curvilinear Component Analysismentioning

confidence: 99%

“…A seed is a couple of neurons made of a neuron and its double, whose weight vector is given by the hard competitive learning, HCL [33,34]. Neuron-doubling is done every time the first-winner is the top of a bridge departing from the second-close neuron (i.e.…”

Section: The Growing Curvilinear Component Analysismentioning

confidence: 99%

“…In case of a stream of non-stationary data, such as those generated for fault and pre-fault diagnosis and modelling, the online Curvilinear Component Analysis (onCCA) and the growing Curvilinear Component Analysis (GCCA) have been proposed in [33][34][35][36]. These two methods exploit incremental quantization to track the non-stationarity; indeed, data clustering is performed together with a fast projection technique based on the Curvilinear Component Analysis (CCA [37,38]).…”

Section: Introductionmentioning

confidence: 99%

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Induction Machine Stator Fault Tracking Using the Growing Curvilinear Component Analysis

et al. 2021

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“…CCA (Demartines & Herault, ; Cirrincione et al, ) is a non‐linear projection method that preserves distance relationships in both input and output spaces. CCA is a useful method for redundant and non‐linear data structure representations and can be used in dimensionality reduction.…”

Section: Techniques Applied To Validate the Proposed Modelmentioning

confidence: 99%

A fault detection system based on unsupervised techniques for industrial control loops

et al. 2019

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This research describes a novel approach for fault detection in industrial processes, by means of unsupervised and projectionist techniques. The proposed method includes a visual tool for the detection of faults, its final aim is to optimize system performance and consequently obtaining increased economic savings, in terms of energy, material, and maintenance. To validate the new proposal, two datasets with different levels of complexity (in terms of quantity and quality of information) have been used to evaluate five well‐known unsupervised intelligent techniques. The obtained results show the effectiveness of the proposed method, especially when the complexity of the dataset is high.

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The on-line curvilinear component analysis (onCCA) for real-time data reduction

Cited by 18 publications

References 6 publications

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Induction Machine Stator Fault Tracking Using the Growing Curvilinear Component Analysis

A fault detection system based on unsupervised techniques for industrial control loops

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