Wrapped Classifier with Dummy Teacher for training physics-based classifier at unlabeled radar data

• URL: http://arxiv.org/abs/2201.05735v1
• Date: Sat, 15 Jan 2022 02:13:58 GMT
• Title: Wrapped Classifier with Dummy Teacher for training physics-based classifier at unlabeled radar data
• Authors: Oleg I.Berngardt, Oleg A.Kusonsky, Alexey I.Poddelsky, Alexey V.Oinats
• Abstract summary: We describe a method for automatic classification of signals received by EKB and MAGW ISTP SB RAS coherent scatter radars during 2021. The method is trained on unlabeled dataset and is based on training optimal physics-based classification using clusterization results. As a result we trained the classification network and found 11 well interpretable classes from physical point view in the available data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the paper a method for automatic classification of signals received by EKB and MAGW ISTP SB RAS coherent scatter radars (8-20MHz operating frequency) during 2021 is described. The method is suitable for automatic physical interpretation of the resulting classification of the experimental data in realtime. We called this algorithm Wrapped Classifier with Dummy Teacher. The method is trained on unlabeled dataset and is based on training optimal physics-based classification using clusterization results. The approach is close to optimal embedding search, where the embedding is interpreted as a vector of probabilities for soft classification. The approach allows to find optimal classification algorithm, based on physically interpretable parameters of the received data, both obtained during physics-based numerical simulation and measured experimentally. Dummy Teacher clusterer used for labeling unlabeled dataset is gaussian mixture clustering algorithm. For algorithm functioning we extended the parameters obtained by the radar with additional parameters, calculated during simulation of radiowave propagation using ray-tracing and IRI-2012 and IGRF models for ionosphere and Earth's magnetic field correspondingly. For clustering by Dummy Teacher we use the whole dataset of available parameters (measured and simulated ones). For classification by Wrapped Classifier we use only well physically interpreted parameters. As a result we trained the classification network and found 11 well-interpretable classes from physical point of view in the available data. Five other found classes are not interpretable from physical point of view, demonstrating the importance of taking into account radiowave propagation for correct classification.

Related papers

• Minimally Supervised Learning using Topological Projections in Self-Organizing Maps [55.31182147885694]
  We introduce a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs) Our proposed method first trains SOMs on unlabeled data and then a minimal number of available labeled data points are assigned to key best matching units (BMU) Our results indicate that the proposed minimally supervised model significantly outperforms traditional regression techniques.
  arXiv  Detail & Related papers  (2024-01-12T22:51:48Z)
• Parametric Classification for Generalized Category Discovery: A Baseline Study [70.73212959385387]
  Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. We investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem. We propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers.
  arXiv  Detail & Related papers  (2022-11-21T18:47:11Z)
• Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving [0.0]
  Unsupervised Domain Adaptation (UDA) approaches are considered low-cost and less time-consuming. We propose UDA schemes using adversarial discriminative and generative methods for lane detection and classification applications in autonomous driving.
  arXiv  Detail & Related papers  (2022-02-15T02:10:14Z)
• Riemannian classification of EEG signals with missing values [67.90148548467762]
  This paper proposes two strategies to handle missing data for the classification of electroencephalograms. The first approach estimates the covariance from imputed data with the $k$-nearest neighbors algorithm; the second relies on the observed data by leveraging the observed-data likelihood within an expectation-maximization algorithm. As results show, the proposed strategies perform better than the classification based on observed data and allow to keep a high accuracy even when the missing data ratio increases.
  arXiv  Detail & Related papers  (2021-10-19T14:24:50Z)
• Unsupervised classification of simulated magnetospheric regions [0.0]
  In magnetospheric missions, burst mode data sampling should be triggered in the presence of processes of scientific or operational interest. We present an unsupervised classification method for magnetospheric regions, that could constitute the first-step of a multi-step method for the automatic identification of magnetospheric processes of interest.
  arXiv  Detail & Related papers  (2021-09-10T14:57:32Z)
• Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering [59.286567680389766]
  Noisy labels are commonly found in real-world data, which cause performance degradation of deep neural networks. We propose Probabilistic Ranking-based Instance Selection with Memory (PRISM) approach for DML. PRISM calculates the probability of a label being clean, and filters out potentially noisy samples.
  arXiv  Detail & Related papers  (2021-08-03T12:15:25Z)
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data [78.69828864672978]
  A central challenge in training classification models in the real-world federated system is learning with non-IID data. We propose a novel and simple algorithm called Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated ssian mixture model. Experimental results demonstrate that CCVR state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10.
  arXiv  Detail & Related papers  (2021-06-09T12:02:29Z)
• Predictive K-means with local models [0.028675177318965035]
  Predictive clustering seeks to obtain the best of the two worlds. We present two new algorithms using this technique and show on a variety of data sets that they are competitive for prediction performance.
  arXiv  Detail & Related papers  (2020-12-16T10:49:36Z)
• Learning with Out-of-Distribution Data for Audio Classification [60.48251022280506]
  We show that detecting and relabelling certain OOD instances, rather than discarding them, can have a positive effect on learning. The proposed method is shown to improve the performance of convolutional neural networks by a significant margin.
  arXiv  Detail & Related papers  (2020-02-11T21:08:06Z)
• Coarse-Grain Cluster Analysis of Tensors with Application to Climate Biome Identification [0.27998963147546146]
  We use the discrete wavelet transform to analyze the effects of coarse-graining on clustering tensor data. We are particularly interested in understanding how scale effects clustering of the Earth's climate system.
  arXiv  Detail & Related papers  (2020-01-22T00:28:58Z)
• Bayesian Semi-supervised Multi-category Classification under Nonparanormality [2.307581190124002]
  Semi-supervised learning is a model training method that uses both labeled and unlabeled data. This paper proposes a fully Bayes semi-supervised learning algorithm that can be applied to any multi-category classification problem.
  arXiv  Detail & Related papers  (2020-01-11T21:31:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.