A feature space transformation method for personalization using generalized i-vector clustering

2012 ◽  
Author(s):  
Kaisheng Yao ◽  
Yifan Gong ◽  
Chaojun Liu
Keyword(s):  
Feature Space ◽  
Transformation Method ◽  
Space Transformation ◽  
Vector Clustering

scholarly journals Feature Space Transformation for Fault Diagnosis of Rotating Machinery under Different Working Conditions

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1417
Author(s):  
Gye-Bong Jang ◽  
Sung-Bae Cho
Keyword(s):  
Fault Diagnosis ◽  
Feature Space ◽  
Transformation Method ◽  
Operating Conditions ◽  
Rotating Machines ◽  
Target Domain ◽  
Practical Applications ◽  
Failure Data ◽  
Space Transformation ◽  
Feature Spaces

In recent years, various deep learning models have been developed for the fault diagnosis of rotating machines. However, in practical applications related to fault diagnosis, it is difficult to immediately implement a trained model because the distribution of source data and target domain data have different distributions. Additionally, collecting failure data for various operating conditions is time consuming and expensive. In this paper, we introduce a new transformation method for the latent space between domains using the source domain and normal data of the target domain that can be easily collected. Inspired by semantic transformations in an embedded space in the field of word embedding, discrepancies between the distribution of the source and target domains are minimized by transforming the latent representation space in which fault attributes are preserved. To match the feature area and distribution, spatial attention is applied to learn the latent feature spaces, and the 1D CNN LSTM architecture is implemented to maximize the intra-class classification. The proposed model was validated for two types of rotating machines such as a dataset of rolling bearings as CWRU and a gearbox dataset of heavy machinery. Experimental results show the proposed method has higher cross-domain diagnostic accuracy than others, therefore showing reliable generalization performance in rotating machines operating under various conditions.

scholarly journals Deep Transformation Method for Discriminant Analysis of Multi-Channel Resting State fMRI

2019 ◽  
Vol 33 ◽  
pp. 2556-2563 ◽  
Author(s):  
Abhay M S Aradhya ◽  
Aditya Joglekar ◽  
Sundaram Suresh ◽  
M. Pratama
Keyword(s):  
Resting State ◽  
Cognitive Disorders ◽  
Feature Space ◽  
Resting State Fmri ◽  
Transformation Method ◽  
Brain Network ◽  
Fmri Data ◽  
Functional Activities ◽  
Temporal Correlations ◽  
Spatio Temporal

Analysis of resting state - functional Magnetic Resonance Imaging (rs-fMRI) data has been a challenging problem due to a high homogeneity, large intra-class variability, limited samples and difference in acquisition technologies/techniques. These issues are predominant in the case of Attention Deficit Hyperactivity Disorder (ADHD). In this paper, we propose a new Deep Transformation Method (DTM) that extracts the discriminant latent feature space from rsfMRI and projects it in the subsequent layer for classification of rs-fMRI data. The hidden transformation layer in DTM projects the original rs-fMRI data into a new space using the learning policy and extracts the spatio-temporal correlations of the functional activities as a latent feature space. The subsequent convolution and decision layers transform the latent feature space into high-level features and provide accurate classification. The performance of DTM has been evaluated using the ADHD200 rs-fMRI benchmark data with crossvalidation. The results show that the proposed DTM achieves a mean classification accuracy of 70.36% and an improvement of 8.25% on the state of the art methodologies was observed. The improvement is due to concurrent analysis of the spatio-temporal correlations between the different regions of the brain and can be easily extended to study other cognitive disorders using rs-fMRI. Further, brain network analysis has been studied to identify the difference in functional activities and the corresponding regions behind cognitive symptoms in ADHD.

scholarly journals Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal Decompositions

2019 ◽  
Vol 9 (17) ◽  
pp. 3566 ◽  
Author(s):  
Francisco-Manuel Melgarejo-Meseguer ◽  
Francisco-Javier Gimeno-Blanes ◽  
María-Eladia Salar-Alcaraz ◽  
Juan-Ramón Gimeno-Blanes ◽  
Juan Martínez-Sánchez ◽  
...  
Keyword(s):  
Visual Information ◽  
Dispersion Coefficient ◽  
Principal Component ◽  
Feature Space ◽  
Component Analysis ◽  
Transformation Method ◽  
Companion Paper ◽  
Enhancement Ratio ◽  
New Paradigm ◽  
Coronary Syndrome

Recent research has proven the existence of statistical relation among fragmented QRS and several highly prevalence diseases, such as cardiac sarcoidosis, acute coronary syndrome, arrythmogenic cardiomyopathies, Brugada syndrome, and hypertrophic cardiomyopathy. One out of five hundred people suffer from hypertrophic cardiomyopathies. The relation among the fragmentation and arrhythmias drives the objective of this work, which is to propose a valid method for QRS fragmentation detection. With that aim, we followed a two-stage approach. First, we identified the features that better characterize the fragmentation by analyzing the physiological interpretation of multivariate approaches, such as principal component analysis (PCA) and independent component analysis (ICA). Second, we created an invariant transformation method for the multilead electrocardiogram (ECG), by scrutinizing the statistical distributions of the PCA eigenvectors and of the ICA transformation arrays, in order to anchor the desired elements in the suitable leads in the feature space. A complete database was compounded incorporating real fragmented ECGs, surrogate registers by synthetically adding fragmented activity to real non-fragmented ECG registers, and standard clean ECGs. Results showed that the creation of beat templates together with the application of PCA over eight independent leads achieves 0.995 fragmentation enhancement ratio and 0.07 dispersion coefficient. In the case of ICA over twelve leads, the results were 0.995 fragmentation enhancement ratio and 0.70 dispersion coefficient. We conclude that the algorithm presented in this work constructs a new paradigm, by creating a systematic and powerful tool for clinical anamnesis and evaluation based on multilead ECG. This approach consistently consolidates the inconspicuous elements present in multiple leads onto designated variables in the output space, hence offering additional and valid visual and non-visual information to standard clinical review, and opening the door to a more accurate automatic detection and statistically valid systematic approach for a wide number of applications. In this direction and within the companion paper, further developments are presented applying this technique to fragmentation detection.

A region-specific feature-space transformation for speaker adaptation and singularity analysis of jacobian matrix

2013 ◽  
Author(s):  
Shakti P. Rath ◽  
Lukáš Burget ◽  
Martin Karafiát ◽  
Ondřej Glembek ◽  
Jan Černocký
Keyword(s):  
Jacobian Matrix ◽  
Feature Space ◽  
Singularity Analysis ◽  
Space Transformation
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