scholarly journals From Complex System Analysis to Pattern Recognition: Experimental Assessment of an Unsupervised Feature Extraction Method Based on the Relevance Index Metrics

Computation ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 39 ◽  
Author(s):  
Laura Sani ◽  
Riccardo Pecori ◽  
Monica Mordonini ◽  
Stefano Cagnoni
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Extraction Method ◽  
System Analysis ◽  
Feature Space ◽  
Extraction Methods ◽  
Feature Extraction Method ◽  
Real World Datasets ◽  
Relevance Index ◽  
Unsupervised Feature Extraction

The so-called Relevance Index (RI) metrics are a set of recently-introduced indicators based on information theory principles that can be used to analyze complex systems by detecting the main interacting structures within them. Such structures can be described as subsets of the variables which describe the system status that are strongly statistically correlated with one another and mostly independent of the rest of the system. The goal of the work described in this paper is to apply the same principles to pattern recognition and check whether the RI metrics can also identify, in a high-dimensional feature space, attribute subsets from which it is possible to build new features which can be effectively used for classification. Preliminary results indicating that this is possible have been obtained using the RI metrics in a supervised way, i.e., by separately applying such metrics to homogeneous datasets comprising data instances which all belong to the same class, and iterating the procedure over all possible classes taken into consideration. In this work, we checked whether this would also be possible in a totally unsupervised way, i.e., by considering all data available at the same time, independently of the class to which they belong, under the hypothesis that the peculiarities of the variable sets that the RI metrics can identify correspond to the peculiarities by which data belonging to a certain class are distinguishable from data belonging to different classes. The results we obtained in experiments made with some publicly available real-world datasets show that, especially when coupled to tree-based classifiers, the performance of an RI metrics-based unsupervised feature extraction method can be comparable to or better than other classical supervised or unsupervised feature selection or extraction methods.

A Novel Feature Extraction Method Based on Collaborative Representation for Face Recognition

2015 ◽  
Vol 29 (04) ◽  
pp. 1556004 ◽  
Author(s):  
Wei Huang ◽  
Xiaohui Wang ◽  
Jianzhong Li ◽  
Zhong Jin
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extraction Method ◽  
High Performance ◽  
Feature Space ◽  
Extraction Methods ◽  
Collaborative Representation ◽  
Feature Extraction Method ◽  
Discriminative Feature ◽  
New Feature

Representation-based classification have received much attention in the field of face recognition. Collaborative representation-based classification (CRC) has shown the robustness and high performance. In this paper, we proposed a new feature extraction method-based collaborative representation. Firstly, we get the coefficients of all face samples by collaborative representation. Then we define the inter-class reconstructive errors and intra-class reconstructive errors for each sample. After that, Fisher criterion is used to get the discriminative feature. At last, CRC is executed to get the identification results in the new feature space. Different from other feature extraction methods, the proposed method integrates the classification criterion into the feature extraction. So the feature space we get fits the classifier better. Experiment results on several face databases show that the proposed method is more effective than other state-of-the-art face recognition methods.

Unsupervised Subspace Extraction via Deep Kernelized Clustering

2021 ◽  
Vol 16 (1) ◽  
pp. 1-15
Author(s):  
Gyoung S. Na ◽  
Hyunju Chang
Keyword(s):  
Feature Extraction ◽  
Side Information ◽  
Feature Space ◽  
Extraction Process ◽  
Extraction Methods ◽  
Feature Extraction Method ◽  
Latent Features ◽  
Benchmark Datasets ◽  
The Given ◽  
Unsupervised Feature Extraction

Feature extraction has been widely studied to find informative latent features and reduce the dimensionality of data. In particular, due to the difficulty in obtaining labeled data, unsupervised feature extraction has received much attention in data mining. However, widely used unsupervised feature extraction methods require side information about data or rigid assumptions on the latent feature space. Furthermore, most feature extraction methods require predefined dimensionality of the latent feature space,which should be manually tuned as a hyperparameter. In this article, we propose a new unsupervised feature extraction method called Unsupervised Subspace Extractor ( USE ), which does not require any side information and rigid assumptions on data. Furthermore, USE can find a subspace generated by a nonlinear combination of the input feature and automatically determine the optimal dimensionality of the subspace for the given nonlinear combination. The feature extraction process of USE is well justified mathematically, and we also empirically demonstrate the effectiveness of USE for several benchmark datasets.

A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

2012 ◽  
Vol 2 (1) ◽  
pp. 16-41 ◽  
Author(s):  
Htwe Pa Pa Win ◽  
Phyo Thu Thu Khine ◽  
Khin Nwe Ni Tun
Keyword(s):  
Feature Extraction ◽  
Structural Analysis ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Extraction Method ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method

This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.

Lung Disease Classification by Novel Shape-Based Feature Extraction and New Hybrid Genetic Approach

2016 ◽  
pp. 321-346
Author(s):  
Bhuvaneswari Chandran ◽  
P. Aruna ◽  
D. Loganathan
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Lung Diseases ◽  
Hybrid Genetic Algorithm ◽  
Extraction Methods ◽  
Disease Classification ◽  
Filter Method ◽  
Svm Classifier ◽  
Genetic Approach ◽  
Feature Extraction Method

The purpose of the chapter is to present a novel method to classify lung diseases from the computed tomography images which assist physicians in the diagnosis of lung diseases. The method is based on a new approach which combines a proposed M2 feature extraction method and a novel hybrid genetic approach with different types of classifiers. The feature extraction methods performed in this work are moment invariants, proposed multiscale filter method and proposed M2 feature extraction method. The essential features which are the results of the feature extraction technique are selected by the novel hybrid genetic algorithm feature selection algorithms. Classification is performed by the support vector machine, multilayer perceptron neural network and Bayes Net classifiers. The result obtained proves that the proposed technique is an efficient and robust method. The performance of the proposed M2 feature extraction with proposed hybrid GA and SVM classifier combination achieves maximum classification accuracy.

Wavelets-Based Feature Extraction for Texture Classification

2010 ◽  
Vol 97-101 ◽  
pp. 1273-1276 ◽  
Author(s):  
Gang Yu ◽  
Ying Zi Lin ◽  
Sagar Kamarthi
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Energy Content ◽  
Texture Classification ◽  
Wavelet Coefficient ◽  
Discrimination Performance ◽  
Extraction Methods ◽  
Rotation Invariant ◽  
Scale Invariant ◽  
Feature Extraction Method

Texture classification is a necessary task in a wider variety of application areas such as manufacturing, textiles, and medicine. In this paper, we propose a novel wavelet-based feature extraction method for robust, scale invariant and rotation invariant texture classification. The method divides the 2-D wavelet coefficient matrices into 2-D clusters and then computes features from the energies inherent in these clusters. The features that contain the information effective for classifying texture images are computed from the energy content of the clusters, and these feature vectors are input to a neural network for texture classification. The results show that the discrimination performance obtained with the proposed cluster-based feature extraction method is superior to that obtained using conventional feature extraction methods, and robust to the rotation and scale invariant texture classification.

scholarly journals Force-Invariant Improved Feature Extraction Method for Upper-Limb Prostheses of Transradial Amputees

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 843
Author(s):  
Md. Johirul Islam ◽  
Shamim Ahmad ◽  
Fahmida Haque ◽  
Mamun Bin Ibne Reaz ◽  
Mohammad Arif Sobhan Bhuiyan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Spatial Correlation ◽  
Extraction Method ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Nonlinear Transformation ◽  
Computational Time ◽  
Feature Extraction Method ◽  
Invariant Feature ◽  
Upper Limb Prostheses

A force-invariant feature extraction method derives identical information for all force levels. However, the physiology of muscles makes it hard to extract this unique information. In this context, we propose an improved force-invariant feature extraction method based on nonlinear transformation of the power spectral moments, changes in amplitude, and the signal amplitude along with spatial correlation coefficients between channels. Nonlinear transformation balances the forces and increases the margin among the gestures. Additionally, the correlation coefficient between channels evaluates the amount of spatial correlation; however, it does not evaluate the strength of the electromyogram signal. To evaluate the robustness of the proposed method, we use the electromyogram dataset containing nine transradial amputees. In this study, the performance is evaluated using three classifiers with six existing feature extraction methods. The proposed feature extraction method yields a higher pattern recognition performance, and significant improvements in accuracy, sensitivity, specificity, precision, and F1 score are found. In addition, the proposed method requires comparatively less computational time and memory, which makes it more robust than other well-known feature extraction methods.

Sign in / Sign up

Export Citation Format

Share Document