Convolutional Self Organizing Map

Dozono, Hiroshi; Niina, Gen; Araki, Satoru

doi:10.1109/csci.2016.0149

Cited by 26 publications

(14 citation statements)

References 2 publications

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“…and its MATLAB implementation in the command cwt, which produces wavelet scalograms that are then fed to a carefully designed "wavelet preconditioning" front-end (see Figure 10 for the overall design). Wavelet scalograms are fed to self-organizing map (SOM) and pooling layers [29] and used as a front end to the MST system to extract features from the wavelet transform and reduce the dimensionality of the input. We performed a variance analysis of the wavelet transform scalogram across the various RF packets to gain insight about the information content of the packet.…”

Section: Wavelet Preconditioningmentioning

confidence: 99%

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Machine Learning Approach to RF Transmitter Identification

Youssef

Bouchard

Haigh

et al. 2018

IEEE J. Radio Freq. Identif.

124

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This document does not contain technology or technical data controlled under either the U.S. International Traffic in Arms Regulations or the U.S. Export Administration Regulations.With the development and widespread use of wireless devices in recent years (mobile phones, Internet of Things, Wi-Fi), the electromagnetic spectrum has become extremely crowded. In order to counter security threats posed by rogue or unknown transmitters, it is important to identify RF transmitters not by the data content of the transmissions but based on the intrinsic physical characteristics of the transmitters. RF waveforms represent a particular challenge because of the extremely high data rates involved and the potentially large number of transmitters present in a given location. These factors outline the need for rapid fingerprinting and identification methods that go beyond the traditional hand-engineered approaches. In this study, we investigate the use of machine learning (ML) strategies to the classification and identification problems, and the use of wavelets to reduce the amount of data required. Four different ML strategies are evaluated: deep neural nets (DNN), convolutional neural nets (CNN), support vector machines (SVM), and multi-stage training (MST) using accelerated Levenberg-Marquardt (A-LM) updates. The A-LM MST method preconditioned by wavelets was by far the most accurate, achieving 100% classification accuracy of transmitters, as tested using data originating from 12 different transmitters. We discuss strategies for extension of MST to a much larger number of transmitters.

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Section: Wavelet Preconditioningmentioning

confidence: 99%

“…The output of this module is sent to the MST stages for classification. Self-organizing map (SOM) [29], an unsupervised ANN method, was used for selecting the filters weights in the convolution layers.…”

Section: Wavelet Preconditioningmentioning

confidence: 99%

Machine Learning Approach to RF Transmitter Identification

Youssef

Bouchard

Haigh

et al. 2018

IEEE J. Radio Freq. Identif.

124

View full text Add to dashboard Cite

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“…Algorithm 4 illustrates this procedure. Connect j to its neighbors; 8 Update the relevances vector of s1: UpdateRelevances(x, s1) (Algorithm 2); 9 else 10 Update the relevances vector of s1: UpdateRelevances(x, s1) (Algorithm 2);…”

Section: F Unsupervised Modementioning

confidence: 99%

“…We point out that prototype-based methods have been successfully applied for both tasks. Methods based on Self-Organizing Maps [2], [5], [6] and K-Means [7] can be highlighted as examples, as well as deep learning techniques [8]- [10]. The Self-Organizing Map (SOM) is an unsupervised learning method, frequently applied for clustering, while Learning Vector Quantization (LVQ) [5], its supervised counterpart that shares many similarities, is normally used for classification.…”

Section: Introductionmentioning

confidence: 99%

A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds

Braga

Bassani

2019

2019 International Joint Conference on Neural Networks (IJCNN)

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In the recent years, there is a growing interest in semi-supervised learning, since, in many learning tasks, there is a plentiful supply of unlabeled data, but insufficient labeled ones. Hence, Semi-Supervised learning models can benefit from both types of data to improve the obtained performance. Also, it is important to develop methods that are easy to parameterize in a way that is robust to the different characteristics of the data at hand. This article presents a new method based on Self-Organizing Map (SOM) for clustering and classification, called Adaptive Local Thresholds Semi-Supervised Self-Organizing Map (ALTSS-SOM). It can dynamically switch between two forms of learning at training time, according to the availability of labels, as in previous models, and can automatically adjust itself to the local variance observed in each data cluster. The results show that the ALTSS-SOM surpass the performance of other semisupervised methods in terms of classification, and other pure clustering methods when there are no labels available, being also less sensitive than previous methods to the parameters values.

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“…A lot of research works have been developed to exploit the advantages of self-organizing map learning in deep architectures such as: Convolutional Recursive Modified Self-Organizing Map (CR-MSOM) [15], Convolutional Self-organing map (CSOM) [16], self-organizing maps with convolutional layers [17], Denoising Autoencoder Self-Organizing Map (DASOM) [18], Unsupervised Deep Self-Organizing Map (UDSOM) [19], [20], Deep Product Self-Organizing Maps (DP-SOM) [21], [22]. However, our proposed Deep Convolutional Self-Organizing Map (DCSOM) model is completely different from the previously mentioned deep SOM architectures.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Self-Organizing Map Network for Robust Handwritten Digit Recognition

Aly

Almotairi

2020

IEEE Access

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Deep Convolutional Neural Networks (DCNN) are currently the predominant technique commonly used to learn visual features from images. However, the complex structure of most recent DCNNs impose two major requirements namely, huge labeled dataset and high computational resources. In this paper, we develop a new efficient deep unsupervised network to learn invariant image representation from unlabeled visual data. The proposed Deep Convolutional Self-organizing Maps (DCSOM) network comprises a cascade of convolutional SOM layers trained sequentially to represent multiple levels of features. The 2D SOM grid is commonly used for either data visualization or feature extraction. However, this work employs high dimensional map size to create a new deep network. The N-Dimensional SOM (ND-SOM) grid is trained to extract abstract visual features using its classical competitive learning algorithm. The topological order of the features learned from ND-SOM helps to absorb local transformation and deformation variations exhibited in the visual data. The input image is divided into an overlapped local patches where each local patch is represented by the N-coordinates of the winner neuron in the ND-SOM grid. Each dimension of the ND-SOM can be considered as a non-linear principal component and hence it can be exploited to represent the input image using N-Feature Index Image (FII) bank. Multiple convolutional SOM layers can be cascaded to create a deep network structure. The output layer of the DCSOM network computes local histograms of each FII bank in the final convolutional SOM layer. A set of experiments using MNIST handwritten digit database and all its variants are conducted to evaluate the robust representation of the proposed DCSOM network. Experimental results reveal that the performance of DCSOM outperforms state-of-the-art methods for noisy digits and achieve a comparable performance with other complex deep learning architecture for other image variations.

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Convolutional Self Organizing Map

Cited by 26 publications

References 2 publications

Machine Learning Approach to RF Transmitter Identification

Machine Learning Approach to RF Transmitter Identification

A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds

Deep Convolutional Self-Organizing Map Network for Robust Handwritten Digit Recognition

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