Outlier Mining Methods Based on Graph Structure Analysis

Amil, Pablo; Almeira, Nahuel; Masoller, Cristina

doi:10.3389/fphy.2019.00194

Cited by 5 publications

(19 citation statements)

References 57 publications

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“…The second algorithm, described in Ref. 10 uses Isomap to learn the low-dimensional manifold in which the images are embedded, and assigns to each image an outlier score by comparing the geodesic distances with the distances in the low-dimensional space. Figure 2 shows the Image Map obtained from the two features returned by Isomap.…”

Section: Unsupervised Machine Learning Algorithmsmentioning

confidence: 99%

“…Figure 4 shows that the performance of the algorithm, measured by the correlation coefficient between the feature returned by the ordering algorithm and the feature provided by manual expert annotation (mean angle), improves when the images that contain artifacts (outliers detected by the outlier detection algorithms proposed in Ref. 10 ) are removed from the database. The different lines show results with different methods of outlier identification and the colored region indicates results when the images removed are randomly selected.…”

Section: Unsupervised Machine Learning Algorithmsmentioning

confidence: 99%

“…8 Here we first present novel algorithms to analyze a database of AS-OCT images: we demonstrate machine learning algorithms to order the images of an AS-OCT image database according to the degree of angle-closure, 9 and also, to identify the images in the database that contain artifacts. 10 In the second part we present novel algorithms for the analysis of color retinal fundus images. 11 These algorithms segment the images and return features of the retinal blood vessel network structure that quantify changes in patients diagnosed with glaucoma or with diabetic retinopathy.…”

Section: Introductionmentioning

confidence: 99%

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New algorithms to characterize and classify ophthalmic images

Amil

Masoller

2020

Biomedical Spectroscopy, Microscopy, and Imaging

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Section: Unsupervised Machine Learning Algorithmsmentioning

confidence: 99%

Section: Unsupervised Machine Learning Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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New algorithms to characterize and classify ophthalmic images

Amil

Masoller

2020

Biomedical Spectroscopy, Microscopy, and Imaging

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“…Many methods for outlier detection have been proposed in the literature (see, e.g. [2][3][4][5][6] and references therein), some of them, based on distances that can be computed between elements of the dataset [7][8][9][10][11]. In outlier detection via graph methods, distance-based outlier mining is based on a fully connected graph structure in which the nodes represent the elements of the dataset and the connections between them are quantified by a distance measure.…”

Section: Introductionmentioning

confidence: 99%

Outlier mining in high-dimensional data using the Jensen–Shannon divergence and graph structure analysis

Toledo

Silini²,

Carpi³

et al. 2022

J. Phys. Complex.

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Reliable anomaly/outlier detection algorithms have practical applications in many fields. For instance, anomaly detection allows to filter and clean the data used to train machine learning (ML) algorithms, improving their performance. However, outlier mining is challenging when the data is high-dimensional, and different approaches have been proposed for different types of data (temporal, spatial, network, etc.). Here we propose a methodology to mine outliers in generic datasets in which it is possible to define a meaningful distance between elements of the dataset. The methodology is based on defining a fully connected, undirected graph, where the nodes are the elements of the dataset and the links have weights that are the distances between the nodes. Outlier scores are defined by analyzing the structure of the graph, in particular, by using the Jensen-Shannon (JS) divergence to compare the distributions of weights of different nodes. We demonstrate the method using a publicly available database of credit-card transactions, where some of the transactions are labeled as frauds. We compare with the performance obtained when using Euclidean distances and graph percolation, and show that the JS divergence leads to performance improvement, but increases the computational cost.

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“…The potential of graph-based methods in combination with machine learning algorithms is addressed in Ref. [5]. The authors explore two methods to detect outliers, with applications to highdimensional datasets.…”

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Editorial: Fundamentals and Applications of AI: An Interdisciplinary Perspective

2021

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Editorial on the Research TopicFundamentals and Applications of AI: An Interdisciplinary Perspective Machines, computers, and algorithms appear recurrently in the future imagined in science fiction pieces. Terminator's Skynet, Space Odyssey's HAL 9000, Psychohistory of Asimov's Foundation, and Westworld's Dolores are just a few examples of our collective imaginary of a (days) topic future. Interestingly, the year 2019 has represented the future in some works. Toronto Star in 1983 asked Asimov to predict the future, the world of 2019. This is also the case for Blade Runner, where the action runs in a dystopic LA, in 2019, with replicants having "almost" human cognitive capabilities. Although we have not reached most of the utopian pictures, the growth of Big Data and Artificial Intelligence algorithms is unquestionable (Figure 1). Thus, celebrating the unstoppable advance of AI, we collect in this RT several studies addressing fundaments and applications from a physics perspective.In 2020, AI has continued its penetration into classical fields and emerging technologies. Fueled by deep learning and the automatic generation of data, the techniques developed in AI are being applied to predict and control physical, biological, engineering, and even commercial systems. Given the two-way interaction between AI and different fields and including how these fields inspire novel methods and theory in AI, we had envisioned a volume illustrating such an interdisciplinary perspective. Contributions include quantum annealers and quantum neural networks, echo state networks, machine learning (reinforcement learning and graph-based methods), and applications to optimization, classification of heartbeats, animal collective movement, and climate forecast, and the use of AI to discover physical laws.A fast machine learning model for ECG-based heartbeat classification and arrhythmia detection was developed, based on echo state networks [1]. The classifier requires a small number of features and a single ECG signal suffices. The possibility of using a combination of ensembles allows them to exploit parallelism to train the classifier with remarkable speed. The sensitivity and predictive values are comparable with those of the state of the art in fully automatic ECG classifiers and even outperform other ECG classifiers that follow more complex feature selection approaches.Reservoir computers are investigated in two contributions. First, a coherent all-optical fiber-ring reservoir computer with distributed Kerr nonlinearity is investigated numerically and experimentally [2]. The system is based on a passive coherent optical fiber-ring cavity where part of the nonlinearity is due to the Kerr effect. They compare the nonlinear transformations of information in the reservoir's input layer, the reservoir itself, and the readout layer. They find that the Kerr effect enhances the computational capability of the reservoir, in particular, its nonlinear computational capacity. Second, the trade-off

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Outlier Mining Methods Based on Graph Structure Analysis

Cited by 5 publications

References 57 publications

New algorithms to characterize and classify ophthalmic images

New algorithms to characterize and classify ophthalmic images

Outlier mining in high-dimensional data using the Jensen–Shannon divergence and graph structure analysis

Editorial: Fundamentals and Applications of AI: An Interdisciplinary Perspective

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