Improving novelty detection using the reconstructions of nearest neighbours

Mesarcik, Michael; Ranguelova, Elena; Boonstra, Albert‐Jan; Nieuwpoort, Rob V. van

doi:10.1016/j.array.2022.100182

Cited by 8 publications

(17 citation statements)

References 21 publications

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“…This is problematic as RFI detection algorithms must produce a Boolean mask of the pixel-precise regions of where RFI is located in a given input. Therefore, we propose to use the Nearest-Latent-Neighbours (NLN) algorithm, an approach that combines both latent measures of difference as well as the pixel-precise reconstruction errors from a generative autoencoding model Mesarcik et al (2022b). This is in contrast to Harrison et al (2019), where particular features are extracted from each spectrogram to determine the novel differences in transmission power and frequency range.…”

Section: Novelty Detection In Radio Astronomymentioning

confidence: 99%

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Learning to detect RFI in radio astronomy without seeing it

Mesarcik,

Boonstra,

Ranguelova

et al. 2022

Preprint

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Radio Frequency Interference (RFI) corrupts astronomical measurements, thus affecting the performance of radio telescopes. To address this problem, supervised segmentation models have been proposed as candidate solutions to RFI detection. However, the unavailability of large labelled datasets, due to the prohibitive cost of annotating, makes these solutions unusable. To solve these shortcomings, we focus on the inverse problem; training models on only uncontaminated emissions thereby learning to discriminate RFI from all known astronomical signals and system noise. We use Nearest-Latent-Neighbours (NLN) -an algorithm that utilises both the reconstructions and latent distances to the nearest-neighbours in the latent space of generative autoencoding models for novelty detection. The uncontaminated regions are selected using weak-labels in the form of RFI flags (generated by classical RFI flagging methods) available from most radio astronomical data archives at no additional cost. We evaluate performance on two independent datasets, one simulated from the HERA telescope and another consisting of real observations from LOFAR telescope. Additionally, we provide a small expert-labelled LOFAR dataset (i.e., strong labels) for evaluation of our and other methods. Performance is measured using AUROC, AUPRC and the maximum F1-score for a fixed threshold. For the simulated data we outperform the current state-of-the-art by approximately 1% in AUROC and 3% in AUPRC for the HERA dataset. Furthermore, our algorithm offers both a 4% increase in AUROC and AUPRC at a cost of a degradation in F1-score performance for the LOFAR dataset, without any manual labelling.

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Section: Novelty Detection In Radio Astronomymentioning

confidence: 99%

“…To solve these problems we propose an unsupervised learning method based on the Nearest Latent Neighbours (NLN) algorithm Mesarcik et al (2022b). This approach leverages novelty detection to perform RFI detection.…”

Section: Introductionmentioning

confidence: 99%

Learning to detect RFI in radio astronomy without seeing it

Mesarcik,

Boonstra,

Ranguelova

et al. 2022

Preprint

Self Cite

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“…The formula for calculating the Euclidean distance can be seen in equation 2.5: The K-Nearest Neighbor method is one of the supervised learning methods whose classification process is based on the distance of the closest neighbors from the training dataset. K-Nearest Neighbor is a method that is often used to implement distance calculations in the classification process because it has a simple formula [17]. The advantages of this method are that it has resistance to training data that has a lot of noise and is effectively used for large training data.…”

Section: K-nearest Neighbormentioning

confidence: 99%

“…In addition, the text will be converted to numbers to be able to proceed to the classification stage. This conversion is carried out using the TF-IDF (Term Frequency-Inverse Document Frequency) method [17].…”

Section: Word Weightingmentioning

confidence: 99%

Auto Labeling to Increase Aspect-Based Sentiment Analysis Using K-Nearest Neighbors Method

2022

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Social media platforms generate many opinions, emotions, and views on all public services. Sentiment analysis is used in various institutions, such as universities, the business industry, and politicians. The evaluation process requires some data, both quantitative and qualitative. Researchers only focus on quantitative data but ignore qualitative data. The evaluation process given by students in the form of a review is qualitative data that is not structured, so it cannot use conventional methods. Unstructured data requires analysis as well as labeling. The labeling process of large amounts of data is a waste of time and money. Data labeling requires very high accuracy to avoid errors. Accuracy in data labeling is used for the process of classifying, training, and testing data. This study aims to automate data labeling using the K-Nearest Neighbors algorithm method. This labeling process can improve the accuracy of sentiment analysis. The results of the classification method can classify responses from Twitter users and can be used by universities as material for evaluating and assessing higher education services. The results of using a confusion matrix with 1.409 data obtained an accuracy rate of 79.43% with a value of k=15

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“…The nearest latent neighbours (NLNs) algorithm proposed by Mesarcik et al. (2022a) introduces a novel approach to RFI detection by framing it as a downstream anomaly detection task based on a generative model trained to represent only noiseless radio data as flagged by AOFlagger produced by Offringa (2010). This methodology significantly reduces the dependence on high-quality flags in the training data, making it more suitable for real-world deployment.…”

Section: Introductionmentioning

confidence: 99%

RFI detection with spiking neural networks

Pritchard,

Wicenec,

Bennamoun

et al. 2024

Publ. Astron. Soc. Aust.

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Detecting and mitigating radio frequency interference (RFI) is critical for enabling and maximising the scientific output of radio telescopes. The emergence of machine learning (ML) methods capable of handling large datasets has led to their application in radio astronomy, particularly in RFI detection. Spiking neural networks (SNNs), inspired by biological systems, are well suited for processing spatio-temporal data. This study introduces the first exploratory application of SNNs to an astronomical data processing task, specifically RFI detection. We adapt the nearest latent neighbours (NLNs) algorithm and auto-encoder architecture proposed by previous authors to SNN execution by direct ANN2SNN conversion, enabling simplified downstream RFI detection by sampling the naturally varying latent space from the internal spiking neurons. Our subsequent evaluation aims to determine whether SNNs are viable for future RFI detection schemes. We evaluate detection performance with the simulated HERA telescope and hand-labelled LOFAR observation dataset the original authors provided. We additionally evaluate detection performance with a new MeerKAT-inspired simulation dataset that provides a technical challenge for machine-learnt RFI detection methods. This dataset focuses on satellite-based RFI, an increasingly important class of RFI and is an additional contribution. Our SNN approach remains competitive with the original NLN algorithm and AOFlagger in AUROC, AUPRC, and F1-scores for the HERA dataset but exhibits difficulty in the LOFAR and Tabascal datasets. However, our method maintains this accuracy while completely removing the compute and memory-intense latent sampling step found in NLN. This work demonstrates the viability of SNNs as a promising avenue for ML-based RFI detection in radio telescopes by establishing a minimal performance baseline on traditional and nascent satellite-based RFI sources and is the first work to our knowledge to apply SNNs in astronomy.

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Improving novelty detection using the reconstructions of nearest neighbours

Cited by 8 publications

References 21 publications

Learning to detect RFI in radio astronomy without seeing it

Learning to detect RFI in radio astronomy without seeing it

Auto Labeling to Increase Aspect-Based Sentiment Analysis Using K-Nearest Neighbors Method

RFI detection with spiking neural networks

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