Nonparametric Feature Extraction Research Articles

Low-resolution face recognition in resource-constrained environments

• A LR face recognition model for resource-constrained environments is proposed. • Our model is lightweight and capable of being effectively trained on small data. • Our model adopts PixelHop++ which is designed based on the SSL principle. • Active learning is incorporated to minimize the required labeled training data. • We show the competitive performance of our model compared with state-of-the-art. Although Deep Neural Networks (DNNs) have achieved tremendous success in the face recognition task, utilizing them in resource-constrained environments with limited networking and computing is challenging. Such environments often demand a small model capable of being effectively trained on a small number of labeled training data, with low training complexity, and low-resolution input images. To address these challenges, we adopt an emerging machine learning methodology called Successive Subspace Learning (SSL) to propose LRFRHop, a high-performance data-efficient low-resolution face recognition model for resource-constrained environments. SSL offers an explainable non-parametric feature extraction submodel that flexibly trades the model size for the verification performance. Its training complexity is significantly lower than DNN-based models since it is trained in a one-pass feedforward manner without backpropagation. Furthermore, active learning can be conveniently incorporated to reduce the labeling cost. We demonstrate the effectiveness of LRFRHop by conducting experiments on the LFW and the CMU Multi-PIE datasets.

Brain-Computer Interface for Persons with Motor Disabilities - A Review

Aim /Objective: A Brain-Computer Interface (BCI) is a communication medium, which restructures brain signals into respective commands for an external device. Methodology: A BCI allows its target users like persons with motor disabilities to act on their environment using brain signals without using peripheral nerves or muscles. In this review article, we have presented a view on different BCIs for humans with motor disabilities. Results & Conclusion: From the study, it is clear that the P300 based Electroencephalography (EEG)BCIs with Steady-State Visually Evoked Potential (SSVEP) non-parametric feature extraction techniques work with high efficiency in the major parameters like Information Bit Transfer Rate (ITR), Mutual Information (MI) rate and Low Signal to Noise Ratio (SNR) and achieve a maximum classification accuracy using Self Organized Fuzzy Neural Network (SOFNN).

Comparison of Parametric and Non-parametric EEG Feature Extraction Methods in Detection of Pediatric Migraine without Aura

Migraine headache without aura is the most common type of migraine especially among pediatric patients. It has always been a great challenge of migraine diagnosis using quantitative electroencephalography measurements through feature classification. It has been proven that different feature extraction and classification methods vary in terms of performance regarding detection and diagnostic accuracy. Previous work on the subject was controversial, hence a comparison of these methods seems necessary. The aim of this research is to compare two parametric and non-parametric feature extraction methods and also two classification methods in order to obtain optimal combinations of diagnostic accuracy. Having recorded background EEG from 24 pediatric migraineurs and 19 control subjects, data was processed by Welch's and Yule-Walker's methods. Features were selected using genetic algorithm, and then given to a support vector machine and the linear discriminant analysis for the classification. Accuracy was calculated for all combinations having the dominant frequency and the correlated absolute power of each EEG wave band (theta, alpha, and beta) and for all wave bands combined. The highest migraine detection accuracy of 93% was obtained utilizing Welch's method for EEG feature extraction alongside support vector machine for a classifier. Besides, Yule-Walker autoregressive method showed better performance than Welch's, when only power bands (and not the dominant frequency) were used as classification input. The superiority of Welch's method over Yule-Walker's and the support vector machine over linear discriminant analysis can be great help for further researches on computer aided EEG-based diagnosis of migraine.

Nonparametric feature extraction for classification of hyperspectral images with limited training samples

Feature extraction plays a crucial role in improvement of hyperspectral images classification. Nonparametric feature extraction methods show better performance compared to parametric ones when distribution of classes is non normal-like. Moreover, they can extract more features than parametric methods do. In this paper, a new nonparametric linear feature extraction method is introduced for classification of hyperspectral images. The proposed method has no free parameter and its novelty can be discussed in two parts. First, neighbor samples are specified by using Parzen window idea for determining local mean. Second, two new weighting functions are used. Samples close to class boundaries will have more weight in the between-class scatter matrix formation and samples close to class mean will have more weight in the within-class scatter matrix formation. The experimental results on three real hyperspectral data sets, Indian Pines, Salinas and Pavia University, demonstrate that the proposed method has better performance in comparison with some other nonparametric and parametric feature extraction methods.

Support vector machine-based stuttering dysfluency classification using GMM supervectors

It is generally acknowledged that recognition and classification of dysfluencies are an important criterion in the objective and accurate assessment of stuttered speech. For this reason, there is a growing interest in the application of Automatic Speech Recognition ASR technology to automate the dysfluency recognition. In this perspective, several studies have been carried out on the classification of dysfluencies by means of acoustic analysis, parametric and non-parametric feature extraction and statistical methods. This work is focused on introducing and evaluating Support Vector Machine SVM based dysfluency recognition system using a Gaussian Mixture Model GMM supervector. The experimental evaluation of the proposed system reveals that an SVM-based GMM supervector is effective for dysfluency classification. We have obtained substantial improvements in the performance by considering cepstral and their delta features.

A Nonparametric Feature Extraction and Its Application to Nearest Neighbor Classification for Hyperspectral Image Data

Feature extraction plays an essential role in hyperspectral image classification. Nonparametric feature extraction algorithms have more advantages than parametric ones and are well suited for nonnormally distributed data along with being able to extract more features than the classic linear discriminant analysis. In this paper, a novel nonparametric feature extraction method, namely, cosine-based nonparametric feature extraction (CNFE), is proposed, in which the weight function embedded in the within-class and between-class scatter matrices is developed based on cosine distance. Moreover, a powerful K-nearest neighbor (KNN) classification algorithm based on the distance metric formed by CNFE features is also developed, which is called the CNFE-based KNN (CKNN) classifier. The effectiveness of the proposed CNFE and CKNN is evaluated by two hyperspectral real data sets. The experimental results demonstrate that both the proposed CNFE and CKNN achieve remarkable performances on various types of training sample sizes, including the small-sample-size cases.

Nonparametric weighted feature extraction for classification

In this paper, a new nonparametric feature extraction method is proposed for high-dimensional multiclass pattern recognition problems. It is based on a nonparametric extension of scatter matrices. There are at least two advantages to using the proposed nonparametric scatter matrices. First, they are generally of full rank. This provides the ability to specify the number of extracted features desired and to reduce the effect of the singularity problem. This is in contrast to parametric discriminant analysis, which usually only can extract L-1 (number of classes minus one) features. In a real situation, this may not be enough. Second, the nonparametric nature of scatter matrices reduces the effects of outliers and works well even for nonnormal datasets. The new method provides greater weight to samples near the expected decision boundary. This tends to provide for increased classification accuracy.