Search results for "Component analysis"
showing 10 items of 562 documents
An overview of incremental feature extraction methods based on linear subspaces
2018
Abstract With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alter…
Extraction of the mismatch negativity elicited by sound duration decrements: A comparison of three procedures
2009
This study focuses on comparison of procedures for extracting the brain event-related potentials (ERPs) - brain responses to stimuli recorded using electroencephalography (EEG). These responses are used to study how the synchronization of brain electrical responses is associated with cognition such as how the brain detects changes in the auditory world. One such event-related response to auditory change is called mismatch negativity (MMN). It is typically observed by computing a difference wave between ERPs elicited by a frequently repeated sound and ERPs elicited by an infrequently occurring sound which differs from the repeated sounds. Fast and reliable extraction of the ERPs, such as the…
A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks
2020
This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at the level of electroencephalogram (EEG) sources. The framework combines the use of common spatial patterns to select the EEG components which maximize the variance between two experimental conditions, simultaneous implementation of vector autoregressive modeling (VAR) with independent component analysis to describe the joint source dynamics and their projection to the scalp, and computation of information dynamics measures (information storage, information transfer, statistically significant network links) from the source VAR parameters. The proposed framework was tested on…
Automatic target recognition using 3D passive sensing and imaging with independent component analysis
2009
We present an overview of a method using Independent Component Analysis (ICA) and 3D Integral Imaging (II) technique to recognize 3D objects at different orientations. This method has been successfully applied to the recognition and classification of 3D scenes.
Three-dimensional object-distortion-tolerant recognition for integral imaging using independent component analysis
2009
Independent component analysis (ICA) aims at extracting unknown components from multivariate data assuming that the underlying components are mutually independent. This technique has been successfully applied to the recognition and classification of objects. We present a method that combines the benefits of ICA and the ability of the integral imaging technique to obtain 3D information for the recognition of 3D objects with different orientations. Our recognition is also possible when the 3D objects are partially occluded by intermediate objects.
Learning with the kernel signal to noise ratio
2012
This paper presents the application of the kernel signal to noise ratio (KSNR) in the context of feature extraction to general machine learning and signal processing domains. The proposed approach maximizes the signal variance while minimizes the estimated noise variance in a reproducing kernel Hilbert space (RKHS). The KSNR can be used in any kernel method to deal with correlated (possibly non-Gaussian) noise. We illustrate the method in nonlinear regression examples, dependence estimation and causal inference, nonlinear channel equalization, and nonlinear feature extraction from high-dimensional satellite images. Results show that the proposed KSNR yields more fitted solutions and extract…
Explicit signal to noise ratio in reproducing kernel Hilbert spaces
2011
This paper introduces a nonlinear feature extraction method based on kernels for remote sensing data analysis. The proposed approach is based on the minimum noise fraction (MNF) transform, which maximizes the signal variance while also minimizing the estimated noise variance. We here propose an alternative kernel MNF (KMNF) in which the noise is explicitly estimated in the reproducing kernel Hilbert space. This enables KMNF dealing with non-linear relations between the noise and the signal features jointly. Results show that the proposed KMNF provides the most noise-free features when confronted with PCA, MNF, KPCA, and the previous version of KMNF. Extracted features with the explicit KMNF…
Cross-Talk Correction Method for Knee Kinematics in Gait Analysis Using Principal Component Analysis (PCA): A New Proposal
2014
International audience; Background: In 3D gait analysis, the knee joint is usually described by the Eulerian way. It consists in breaking down the motion between the articulating bones of the knee into three rotations around three axes: flexion/extension, abduction/adduction and internal/external rotation. However, the definition of these axes is prone to error, such as the "cross-talk'' effect, due to difficult positioning of anatomical landmarks. This paper proposes a correction method, principal component analysis (PCA), based on an objective kinematic criterion for standardization, in order to improve knee joint kinematic analysis. Methods: The method was applied to the 3D gait data of …
ChemInform Abstract: Chemometrics: An Important Tool for the Modern Chemist, an Example from Wood-Processing Chemistry.
2010
This study briefly outlines the idea of principal component analysis and cross-correlation calculations (applied chemometrics) and presents an illustrative example from wood-processing chemistry. The applicability of chemometric data analysis was demonstrated by investigating the various structural changes that take place in dissolved and degraded lignin ("kraft lignin") during laboratory-scale kraft pulping of Scots pine (Pinus sylvestris) and silver birch (Betula pendula). The structural data (31P NMR and size exclusion chromatographic data) on kraft lignin were further processed by chemometric multivariate techniques (PCA and 2DCC), confirming, for example, that the cleavage of beta-aryl…
Chemometrics: An Important Tool for the Modern Chemist, an Example from Wood-Processing Chemistry
2000
This study briefly outlines the idea of principal component analysis and cross-correlation calculations (applied chemometrics) and presents an illustrative example from wood-processing chemistry. The applicability of chemometric data analysis was demonstrated by investigating the various structural changes that take place in dissolved and degraded lignin ("kraft lignin") during laboratory-scale kraft pulping of Scots pine (Pinus sylvestris) and silver birch (Betula pendula). The structural data (31P NMR and size exclusion chromatographic data) on kraft lignin were further processed by chemometric multivariate techniques (PCA and 2DCC), confirming, for example, that the cleavage of beta-aryl…