A Recent Study on High Dimensional Features Used in Stego Image Anomaly Detection

2018 ◽  
pp. 49-66
Author(s):  
Hemalatha J ◽  
KavithaDevi M.K. ◽  
Geetha S.
Keyword(s):  
Feature Extraction ◽  
Anomaly Detection ◽  
High Dimension ◽  
Digital Images ◽  
Extraction Methods ◽  
High Dimensional ◽  
Extraction Techniques ◽  
Stego Image ◽  
Feature Spaces ◽  
Higher Dimensional

This chapter describes how ample feature extraction techniques are available for detecting hidden messages in digital images. In the recent years, higher dimensional features are extracted to detect the complex and advanced steganographic algorithms. To improve the precision of steganalysis, many combinations of high dimension feature spaces are used by recent steganalyzers. In this chapter, we present a summary of several methods existing in literature. The aim is to provide a broad introduction to high dimensional features space used so for and to state which the most accurate and best feature extraction methods is.

scholarly journals ECG Beats Classification Using Mixture of Features

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Manab Kumar Das ◽  
Samit Ari
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Ectopic Beat ◽  
Extraction Techniques ◽  
Efficient System ◽  
Ecg Signals ◽  
Temporal Features ◽  
S Transform ◽  
Electrocardiogram Ecg

Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.

scholarly journals Membership-Degree Preserving Discriminant Analysis with Applications to Face Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Zhangjing Yang ◽  
Chuancai Liu ◽  
Pu Huang ◽  
Jianjun Qian
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
High Dimensional ◽  
Membership Degree ◽  
Extraction Techniques ◽  
Fisher Criterion ◽  
Extraction Algorithm ◽  
Definition Of

In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data. In this paper, we propose a novel feature extraction algorithm called membership-degree preserving discriminant analysis (MPDA) based on the fisher criterion and fuzzy set theory for face recognition. In the proposed algorithm, the membership degree of each sample to particular classes is firstly calculated by the fuzzyk-nearest neighbor (FKNN) algorithm to characterize the similarity between each sample and class centers, and then the membership degree is incorporated into the definition of the between-class scatter and the within-class scatter. The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied. Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm.

Study Speech Recognition System Based on Manifold Learning

2013 ◽  
Vol 380-384 ◽  
pp. 3762-3765
Author(s):  
Peng Hao Zhang
Keyword(s):  
Feature Extraction ◽  
Manifold Learning ◽  
High Dimension ◽  
Geodesic Distance ◽  
Recognition System ◽  
Extraction Methods ◽  
Speech Recognition System ◽  
Phonetic Feature ◽  
Learning Speed ◽  
Distance Data

This paper conducts a comprehensive research and discussion on the relevant technologies and manifold learning.Traditional MFCC phonetic feature will lead a slower learning speed on account of it has high dimension and is large in data quantities. In order to solve this problem, we introduce a manifold learning, putting forward two new extraction methods of MFCC-Manifold phonetic feature. We can reduce dimensions by making use of ISOMAP algorithm which bases on the classical MDS (Multidimensional scaling). Introducing geodesic distance to replace the original European distance data will make twenty-four dimensional data, which using the traditional MFCC feature extraction down to ten dimensional data.

Differentiation of journal bearing friction states and friction intensities based on feature extraction methods applied on acoustic emission signals

2017 ◽  
Vol 84 (s1) ◽  
Author(s):  
Noushin Mokhtari ◽  
Farid Rahbar ◽  
Clemens Gühmann
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Journal Bearing ◽  
Extraction Methods ◽  
Mechatronic Systems ◽  
Mixed Friction ◽  
Feature Spaces ◽  
Prediction Time ◽  
Ae Signals ◽  
Bearing Friction

AbstractFor diagnosis and predictive maintenance of mechatronic systems, monitoring of bearings is essential. An important building block for this is the determination of the bearing friction condition. This paper deals with the possibility of monitoring different journal bearing friction states, such as mixed and fluid friction, and examines a new approach to distinguish between different friction intensities under several speed and load combinations based on feature extraction and feature selection methods applied on acoustic emission (AE) signals. The aim of this work is to identify separation effective features of AE signals to subsequently classify the journal bearing friction states. Furthermore, the acquired features give information about the mixed friction intensity, which is significant for remaining useful lifetime (RUL) prediction. Time domain features as well as features in the frequency domain have been investigated in this work. The combination of several features generates feature spaces. The position of the objects within these spaces has proved that it is possible to differentiate between journal bearing friction states with the use of AE signals and suitable feature extraction methods. In addition, features that indicate different mixed friction intensities have been found.

scholarly journals On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Elisabeth K¨allstr¨om ◽  
Tomas Olsson ◽  
John Lindstr¨om ◽  
Lars Hakansson ◽  
Jonas Larsson
Keyword(s):  
Feature Extraction ◽  
Anomaly Detection ◽  
Extraction Methods ◽  
Continuous Queries ◽  
Case Based Reasoning ◽  
Heavy Duty ◽  
Data Stream Management ◽  
Detection And Diagnosis ◽  
Case Based ◽  
Slippage Detection

In order to reduce unnecessary stops and expensive downtime originating from clutch failure of construction equipment machines; adequate real time sensor data measured on the machine in combination with feature extraction and classification methods may be utilized.This paper presents a framework with feature extraction methods and an anomaly detection module combined with Case-Based Reasoning (CBR) for on-board clutch slippage detection and diagnosis in heavy duty equipment. The feature extraction methods used are Moving Average Square Value Filtering (MASVF) and a measure of the fourth order statistical properties of the signals implemented as continuous queries over data streams. The anomaly detection module has two components, the Gaussian Mixture Model (GMM) and the Logistics Regression classifier. CBR is a learning approach that classifies faults by creating a new solution for a new fault case from the solution of the previous fault cases. Through use of a data stream management system and continuous queries (CQs), the anomaly detection module continuously waits for a clutch slippage event detected by the feature extraction methods, the query returns a set of features, which activates the anomaly detection module. The first component of the anomaly detection module trains a GMM to extracted features while the second component uses a Logistic Regression classifier for classifying normal and anomalous data. When an anomaly is detected, the Case-Based diagnosis module is activated for fault severity estimation.

scholarly journals Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study

2020 ◽  
Vol 21 (6) ◽  
pp. 2181 ◽  
Author(s):  
Chao Feng ◽  
Shufen Liu ◽  
Hao Zhang ◽  
Renchu Guan ◽  
Dan Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Dimension Reduction ◽  
Single Cell ◽  
Rna Sequencing ◽  
Sparse Matrix ◽  
Component Analysis ◽  
Extraction Methods ◽  
High Dimensional ◽  
Single Cell Rna Sequencing ◽  
Reduction Methods

With recent advances in single-cell RNA sequencing, enormous transcriptome datasets have been generated. These datasets have furthered our understanding of cellular heterogeneity and its underlying mechanisms in homogeneous populations. Single-cell RNA sequencing (scRNA-seq) data clustering can group cells belonging to the same cell type based on patterns embedded in gene expression. However, scRNA-seq data are high-dimensional, noisy, and sparse, owing to the limitation of existing scRNA-seq technologies. Traditional clustering methods are not effective and efficient for high-dimensional and sparse matrix computations. Therefore, several dimension reduction methods have been introduced. To validate a reliable and standard research routine, we conducted a comprehensive review and evaluation of four classical dimension reduction methods and five clustering models. Four experiments were progressively performed on two large scRNA-seq datasets using 20 models. Results showed that the feature selection method contributed positively to high-dimensional and sparse scRNA-seq data. Moreover, feature-extraction methods were able to promote clustering performance, although this was not eternally immutable. Independent component analysis (ICA) performed well in those small compressed feature spaces, whereas principal component analysis was steadier than all the other feature-extraction methods. In addition, ICA was not ideal for fuzzy C-means clustering in scRNA-seq data analysis. K-means clustering was combined with feature-extraction methods to achieve good results.

scholarly journals A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Zena M. Hira ◽  
Duncan F. Gillies
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Microarray Data ◽  
Extraction Methods ◽  
Computational Time ◽  
High Dimensional ◽  
Gene Expressions ◽  
Advantages And Disadvantages

We summarise various ways of performing dimensionality reduction on high-dimensional microarray data. Many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accurate. A popular source of data is microarrays, a biological platform for gathering gene expressions. Analysing microarrays can be difficult due to the size of the data they provide. In addition the complicated relations among the different genes make analysis more difficult and removing excess features can improve the quality of the results. We present some of the most popular methods for selecting significant features and provide a comparison between them. Their advantages and disadvantages are outlined in order to provide a clearer idea of when to use each one of them for saving computational time and resources.

scholarly journals A high-dimensionality-trait-driven learning paradigm for high dimensional credit classification

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Lean Yu ◽  
Lihang Yu ◽  
Kaitao Yu
Keyword(s):  
Feature Extraction ◽  
Credit Risk ◽  
Sample Size ◽  
Extraction Methods ◽  
Typical Feature ◽  
High Dimensionality ◽  
High Dimensional ◽  
Learning Paradigm ◽  
Classifier Selection ◽  
Definition Of

AbstractTo solve the high-dimensionality issue and improve its accuracy in credit risk assessment, a high-dimensionality-trait-driven learning paradigm is proposed for feature extraction and classifier selection. The proposed paradigm consists of three main stages: categorization of high dimensional data, high-dimensionality-trait-driven feature extraction, and high-dimensionality-trait-driven classifier selection. In the first stage, according to the definition of high-dimensionality and the relationship between sample size and feature dimensions, the high-dimensionality traits of credit dataset are further categorized into two types: 100 < feature dimensions < sample size, and feature dimensions ≥ sample size. In the second stage, some typical feature extraction methods are tested regarding the two categories of high dimensionality. In the final stage, four types of classifiers are performed to evaluate credit risk considering different high-dimensionality traits. For the purpose of illustration and verification, credit classification experiments are performed on two publicly available credit risk datasets, and the results show that the proposed high-dimensionality-trait-driven learning paradigm for feature extraction and classifier selection is effective in handling high-dimensional credit classification issues and improving credit classification accuracy relative to the benchmark models listed in this study.

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