Multifeature Metric Learning Based on Enhanced Equidistance Embedding for Electroencephalogram Recognition of Epilepsy

Mobile edge computing (MEC) has the ability of pattern recognition and intelligent processing of real-time data. Electroencephalogram (EEG) is a very important tool in the study of epilepsy. It provides rich information that can not be provided by other physiological methods. In the automatic classification of EEG signals by intelligent algorithms, feature extraction and the establishment of classifiers are both very important steps. Different feature extraction methods, such as time domain, frequency domain, and nonlinear dynamic feature methods, contain independent and diverse specific information. Using multiple forms of features at the same time can improve the accuracy of epilepsy recognition. In this paper, we apply metric learning to epileptic EEG signal recognition. Inspired by the equidistance constrained metric learning algorithm, we propose multifeature metric learning based on enhanced equidistance embedding (MMLE3) for EEG recognition of epilepsy. The MMLE3 algorithm makes use of various forms of EEG features, and the feature weights are adaptively weighted. It is a big advantage that the feature weight vector can be adjusted adaptively, without manual adjustment. The MMLE3 algorithm maximizes the distance between the samples constrained by the cannot-link, and the samples of different classes are transformed into equidistant; meanwhile, MMLE3 minimizes the distance between the data constrained by the must-link, and the samples of the same class are compressed to one point. Under the premise that the various feature classification tasks are consistent, MMLE3 can fully extract the associated and complementary information hidden between the features. The experimental results on the CHB-MIT dataset verify that the MMLE3 algorithm has good generalization performance.

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A REVIEW OF FEATURE EXTRACTION METHODS ON MACHINE LEARNING

Journal of Information System and Technology Management ◽

10.35631/jistm.622005 ◽

2021 ◽

Vol 6 (22) ◽

pp. 51-59

Author(s):

Mustazzihim Suhaidi ◽

Rabiah Abdul Kadir ◽

Sabrina Tiun

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Feature Selection ◽

Input Data ◽

Feature Vector ◽

Learning Algorithm ◽

Extraction Methods ◽

Machine Learning Algorithm ◽

Learning Tasks ◽

Low Dimensional

Extracting features from input data is vital for successful classification and machine learning tasks. Classification is the process of declaring an object into one of the predefined categories. Many different feature selection and feature extraction methods exist, and they are being widely used. Feature extraction, obviously, is a transformation of large input data into a low dimensional feature vector, which is an input to classification or a machine learning algorithm. The task of feature extraction has major challenges, which will be discussed in this paper. The challenge is to learn and extract knowledge from text datasets to make correct decisions. The objective of this paper is to give an overview of methods used in feature extraction for various applications, with a dataset containing a collection of texts taken from social media.

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Deep learning for constructing microblog behavior representation to identify social media user's personality

10.7287/peerj.preprints.1906 ◽

2016 ◽

Author(s):

Xiaoqian Liu ◽

Tingshao Zhu

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Prediction Model ◽

Learning Algorithm ◽

Rapid Development ◽

Feature Learning ◽

Extraction Methods ◽

Deep Learning Algorithm ◽

Personality Prediction ◽

Behavior Description

Due to the rapid development of information technology, Internet has become part of everyday life gradually. People would like to communicate with friends to share their opinions on social networks. The diverse social network behavior is an ideal users' personality traits reflection. Existing behavior analysis methods for personality prediction mostly extract behavior attributes with heuristic. Although they work fairly well, but it is hard to extend and maintain. In this paper, for personality prediction, we utilize deep learning algorithm to build feature learning model, which could unsupervised extract Linguistic Representation Feature Vector (LRFV) from text published on Sina Micro-blog actively. Compared with other feature extraction methods, LRFV, as an abstract representation of Micro-blog content, could describe use's semantic information more objectively and comprehensively. In the experiments, the personality prediction model is built using linear regression algorithm, and different attributes obtained through different feature extraction methods are taken as input of prediction model respectively. The results show that LRFV performs more excellently in micro-blog behavior description and improve the performance of personality prediction model.

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Deep learning for constructing microblog behavior representation to identify social media user's personality

10.7287/peerj.preprints.1906v1 ◽

2016 ◽

Author(s):

Xiaoqian Liu ◽

Tingshao Zhu

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Prediction Model ◽

Learning Algorithm ◽

Rapid Development ◽

Feature Learning ◽

Extraction Methods ◽

Deep Learning Algorithm ◽

Personality Prediction ◽

Behavior Description

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Gearbox Fault Detection Using Neural Networks Technology

Volume 7A: 17th Biennial Conference on Mechanical Vibration and Noise ◽

10.1115/detc99/vib-8329 ◽

1999 ◽

Author(s):

Qiao Sun ◽

Xiaolei Li ◽

Baoyun Xu

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Feature Extraction ◽

Fault Diagnosis ◽

Learning Algorithm ◽

Extraction Methods ◽

Bp Network ◽

Speed Increase ◽

Learning Speed ◽

Hidden Layer

Abstract This paper describes the application of neural networks to gearbox fault diagnosis. In order to increase learning speed of BP network, a modified learning algorithm was presented. Considering of the difficulty of choosing neural networks’ architecture, genetic algorithm was employed. The discussion of the effect of hidden layer nodes shows that with the increase of the number of nodes, the learning speed increase also yet result in poor generalization ability. The test of fault tolerance ability tells that neural networks have ‘bench type’ tolerance ability. This ensures that when signals were contaminated by noise or feature extraction methods were not effective, the result can still be acceptable. To test the performance of the application of neural networks on gearbox fault diagnosis, experiments of single fault and multi-faults were both implemented and diagnosed by neural networks. The results were satisfied.

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Collaborative Use of Features in a Distributed System for the Organization of Music Collections

Intelligent Music Information Systems ◽

10.4018/978-1-59904-663-1.ch007 ◽

2011 ◽

pp. 147-175 ◽

Cited By ~ 3

Author(s):

Ingo Mierswa

Keyword(s):

Feature Extraction ◽

Learning Algorithm ◽

Building Blocks ◽

Extraction Methods ◽

Media Management ◽

Learning Tasks ◽

Personalized Services ◽

Meta Learning ◽

Adaptive Feature Extraction ◽

Complex Extraction

Today, large audio collections are stored at computers. Their organization can be supported by machine learning. This demands a more abstract representation than is the time series of audio values. We have developed a unifying framework which decomposes the complex extraction methods into their building blocks. This allows us to move beyond the manual composition of feature extraction methods. Several of the well-known features as well as some new ones have been composed automatically by a genetic learning algorithm. While this has delivered good classifications it needs long training times. Hence, we additionally follow a meta-learning approach. We have developed a method of feature transfer which exploits the similarity of learning tasks to retrieve similar feature extractions. This method achieves almost optimal accuracies while it is very efficient. Nemoz, an intelligent media management system, incorporates adaptive feature extraction and feature transfer which allows for personalized services in peer-to-peer settings.

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A kernel-based learning algorithm combining kernel discriminant coordinates and kernel principal components

Biometrical Letters ◽

10.2478/bile-2014-0005 ◽

2014 ◽

Vol 51 (1) ◽

pp. 57-73 ◽

Cited By ~ 2

Author(s):

Karol Deręgowski ◽

Mirosław Krzyśko

Keyword(s):

Pattern Recognition ◽

Feature Extraction ◽

Principal Components ◽

Learning Algorithm ◽

Extraction Methods ◽

Kernel Trick ◽

New Learning ◽

Nonlinear Feature Extraction ◽

Nonlinear Feature

SUMMARY Kernel principal components (KPC) and kernel discriminant coordinates (KDC), which are the extensions of principal components and discriminant coordinates, respectively, from a linear domain to a nonlinear domain via the kernel trick, are two very popular nonlinear feature extraction methods. The kernel discriminant coordinates space has proven to be a very powerful space for pattern recognition. However, further study shows that there are still drawbacks in this method. To improve the performance of pattern recognition, we propose a new learning algorithm combining the advantages of KPC and KDC

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Feature extraction and prediction of Dengue Outbreaks

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206544 ◽

2020 ◽

pp. 216-222

Author(s):

Kunal Parikh ◽

Tanvi Makadia ◽

Harshil Patel

Keyword(s):

Public Health ◽

Machine Learning ◽

Developing Countries ◽

Feature Extraction ◽

Predictive Analytics ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Health Concerns ◽

The World ◽

Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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