Theory Of Independent Component Analysis Research Articles

Estimation of non-Gaussian SVAR models: a pseudo-log-likelihood function approach

We consider estimation problem in structural vector autoregressive model which disturbance has non-Gaussian distribution. We call this model as non-Gaussian vector autoregressive (NG-SVAR) model. Since the estimation problem of this model is closely related to the independent component analysis (ICA) developed in machine learning and signal processing we apply the theory of ICA to our estimation problem. However, since we do not know the true non-Gaussian distribution in practice, we cannot construct the exact loglikelihood function. In this paper we propose a pseudo maximum loglikelihood estimator instead. It is shown that our estimator is statistical efficient from view point of semiparametric statistics. Furthermore, we show that our estimator has satisfactory performance by Monte Carlo experiment and empirical example in small sample.

An effective estimation strategy for significant wave height via independent component analysis

In this paper, an effective significant wave height (SWH) estimation method of X-band radar image sequences based on independent component analysis (ICA) is proposed. Radial direction data is first selected from the radar image, and then the original components of ocean waves are obtained via the ICA theory. Considering the formation mechanism of ocean waves, the relationship between SWH and energy of ocean waves is explored, which is propitious for a more effective estimation of wave height. Finally, by adopting the fitted relationship, SWH is estimated by calculating the energy of waves. The proposed method is tested by the simulated data and the real data. The results show that the proposed method achieves better correlation coefficient between the real value and the estimation value, which is more than 92%. The root mean squared error (RMSE) is lower than the EOF-based method and the EEMD-based method. The results indicate that the performance of the proposed method has been improved in terms of significant wave height estimation.

Applications of Independent Component Analysis in Wireless Communication Systems

Independent component analysis (ICA) is a signal processing technique used for separating statistically independent and non-Gaussian mixed signals. It is widely used in different areas e.g., wireless communication, speech and biomedical signal processing, vibration analysis, and machinery fault diagnosis. In wireless communication systems, ICA has been used in multiple input multiple output systems, wireless sensor networks, cognitive radio networks, code division multiple access , and orthogonal frequency division multiplexing. The applications of ICA in wireless communication include the suppression of inter symbol interference, cancellation of inter channel interference, direction of arrival estimation, automatic classification of modulation, and spectrum sensing etc. This paper provides a comprehensive survey on the applications of ICA in various wireless communication systems along with the survey of mixing models used in the theory of ICA. The techniques for estimating the number of signals in received mixed signals are also studied. We also surveyed the ICA applications in time varying mixing scenario. The challenges and limitations of ICA regarding wireless communication systems are also presented. This paper also outlines future research directions related to the field of applications of ICA in wireless communication systems.

Study of Multivariate Data Clustering Based on K-Means and Independent Component Analysis

For last two decades, clustering is well-recognized area in the research field of data mining. Data clustering plays the major research at pattern recognition, Signal processing, bioinformatics and Artificial Intelligence. Clustering process is an unsupervised learning techniques where it generates a group of object based on their similarity in such a way that the objects belonging to other groups are similar and those belonging to other are dissimilar. This paper analysis the three different data types clustering techniques like K-Means, Principal components analysis (PCA) and Independent component analysis (ICA) in real and simulated data. The recent developments by considering a rather unexpected application of the theory of Independent component analysis (ICA) found in data clustering, outlier detection and multivariate data visualization. Accurate identification of data clustering plays an important role in statistical analysis. In this paper we explore the connection among these three techniques to identify multivariate data clustering and develop a new method k-means on PCA or ICA then the result shows that ICA based clustering performs well than others.

Multivariate Outlier Detection Using Independent Component Analysis

The recent developments by considering a rather unexpected application of the theory of Independent component analysis (ICA) found in outlier detection, data clustering and multivariate data visualization etc. Accurate identification of outliers plays an important role in statistical analysis. If classical statistical models are blindly applied to data containing outliers, the results can be misleading at best. In addition, outliers themselves are often the special points of interest in many practical situations and their identification is the main purpose of the investigation. This paper takes an attempt a new and novel method formultivariate outlier detection using ICA and compares with different outlier detection techniques in the literature.

A phase space reconstruction based single channel ICA algorithm and its application in dam deformation analysis

Separating noise, systematic errors and deformation components that are caused by different forces such as water pressure and temperature should be identified from dam deformation monitoring data to facilitate effective dam deformation analysis and consequently determine the potential dam structural damage. As source signals are assumed to be mutually independent, independent component analysis (ICA) can be used to separate the source signals from their mixtures. In addition to signal independence, another requirement of ICA theory is that the number of mixtures should not be less than that of source signals. Unfortunately, in most cases of dam deformation monitoring, only a single channel of observed displacement data is available for each monitoring point. Therefore, ICA with single channel data input (called single channel ICA) is necessary in the application of deformation analysis. In this paper, we apply a phase space reconstruction based single channel ICA (PSR-ICA) algorithm to de-noise deformation monitoring data and separate deformation components introduced by different forces from these de-noised data. A numerical simulation is conducted, and results indicate that PSR-ICA is an efficient tool not only for denoising data but also for separating deformation components caused by different forces. PSR-ICA is then further utilised to process the displacement monitoring data of Wuqiangxi Dam. Results indicate that the two extracted main displacement components are nearly consistent with the displacement components; temperature and water level are used as variables and are computed by using a regression model. Both numerical simulated and real life dams demonstrate that PSR-ICA is an effective tool for separating deformation components by different causative forces and is therefore beneficial to dam deformation analysis.

Independent component analysis: recent advances.

Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of non-Gaussianity, which enables the identification of original, underlying components, in contrast to classical methods. The basic theory of independent component analysis was mainly developed in the 1990s and summarized, for example, in our monograph in 2001. Here, we provide an overview of some recent developments in the theory since the year 2000. The main topics are: analysis of causal relations, testing independent components, analysing multiple datasets (three-way data), modelling dependencies between the components and improved methods for estimating the basic model.

TARGET DETECTION IN PULSE-TRAIN MIMO RADARS APPLYING ICA ALGORITHMS

In this paper, the problem of target detection in co- located \multi-input multi-output (MIMO) radars is considered. A pulse-train signaling is assumed to be used in this system. As the doppler efiect should be considered for the pulse-train signaling, we are confronted by a compound hypothesis testing problem, so in this paper a Generalized Likelihood Ratio (GLR) detector is derived. The high complexity of this detector makes us derive a new detector based on the theory of Independent Component Analysis (ICA). It is shown that the computational load of the ICA-based detector is much less than the GLR detector. It is also shown that the sensitivity of the ICA-based detector to the doppler efiect is very low. According to this approach, an appropriate signal design method is presented, based on the separation performance of the ICA algorithms. It is shown that independent random sequences are proper signals in the sense of detection performance.

Separation of Bridge Deflection Signals Based on ICA

In order to reject environmental factor effect from long-span bridge deflection signal, this document introduced the basic theory of independent component analysis (ICA) and applied FastICA algorithm to separate background engineering simulation signal, which was based on using vector-dimension-augmenting technique to process the signal after adopting fir lowpass filter to rejecte the high frequency component, and finally relized the separations of live load deflection, temperature difference deflection and long-term deflection. Campared separation results with deflection source signal, it was shown that the separation result was good when the correlation coefficients between separation signal and source signal were larger than 0.8.

Mixed Vibration Signal Separation and Moving Object Detection Based on Independent Component Analysis

This article briefly describes the basic theory of independent component analysis (ICA) and algorithms. Independent component analysis (ICA) method is employed to separate the mixed vibration signal, measured from linear sensor array. By calculating the spatial spectrum function, identification and tracking of multiple moving targets achieved. The results show that, ICA can successfully detect and track multiple targets.

A Gabor subband decomposition ICA and MRF hybrid algorithm for infrared image reconstruction from subpixel shifted sequences

An image blind reconstruction, as a blind source separation problem, has been solved recently by independent component analysis (ICA). Based on ICA theory, in this paper, a high resolution image is reconstructed from low resolution and subpixel shifted sequences captured by infrared microscan imaging system. The algorithm has the attractive feature that neither the prior knowledge of the blur kernel nor the value of subpixel misregistrations between the input channels is required. The statistical independence in the image domain is improved by the multiscale Gabor subband decompositions, which are designed for the best ability to cover the whole spatial frequency and to avoid overlapping between the subbands. The mutual information is employed to locate a subband with the least dependent components. In terms of MAP estimator, we combine the super-Gaussian with Markov random field to form a hybrid image distribution. This strategy helps to estimate the separating matrix reasonable to extract the sources with the image properties, that is, sharp enough as well as correlative in local area. The proposed algorithm is capable of performing high resolution image sources which are not strictly independent, and its viability is proved by the computer simulations and real experiments.

독립성분분석을 이용한 혼합물의 미지성분비율 예측

독립성분분석은 차원이 높은 다변량데이타로부터 기저구조를 형성하는 독립성분을 분리하는데 사용되는 기법으로서 패턴인식, 예측 등 2차적 분석을 위한 1차 분석단계에서 사용할 수 있다. 본 연구에서는 독립성분분석을 이용하여 여러 혼합물 데이터로부터 독립성분을 분리한 다음 각 구성성분의 혼합비율을 예측하는 절차를 제안한다. 적용예로서 도금강판의 엑스선 회절강도값으로부터 여러가지 상을 분리한 다음 비음최소자승법을 이용하여 각 상의 분율을 예측하였으며, 이러한 제안방안의 타당성 평가를 위하여 모의 실험을 실시하였다. Independent component analysis (ICA) is a statistical method for transforming an observed high-dimensional multivariate data into statistically independent components. ICA has been applied increasingly in wide fields of spectrum application since ICA is able to extract unknown components of a mixture from spectra. We focus on application of ICA for separating independent sources and predicting each composition using extracted components. The theory of ICA is introduced and an application to a metal surface spectra data will be described, where subsequent analysis using non-negative least square method is performed to predict composition ratio of each sample. Furthermore, some simulation experiments are performed to demonstrate the performance of the proposed approach.

Independent component analysis and regression applied on sensory data

AbstractIn this paper, independent component analysis (ICA) and a partial least squares implementation of independent component regression (ICR) were applied on two sensory data sets. A brief introduction to the theory of ICA is presented, and how to find the optimal model rank is discussed. The effect of the number of independent components extracted is illustrated by comparison of ICA loadings from models with different numbers of components. Principal component analysis (PCA) and ICA were employed on the sensory data, and these methods are interpreted based on explained variance for the components and groupings of the sensory attributes. Significance testing on each sensory attribute for the components gave valuable information about the relevance in interpreting the individual attributes and components. ICA was also applied for regression purposes similarly to principal component regression (PCR) and partial least squares regression (PLSR). An algorithm which combines ICA and PLSR (IC‐PLSR) is presented. Single‐response IC‐PLSR seemed to be a promising complementary method to PLSR in extracting informative and valid components. Copyright © 2005 John Wiley & Sons, Ltd.

Cross validation and uncertainty estimates in independent component analysis

A data analysis tool, known as independent component analysis (ICA), is the main focus of this paper. The theory of ICA is briefly reviewed, and the underlying statistical assumptions and a practical algorithm are described. This paper introduces cross validation/jack-knifing and significance tests to ICA. Jack-knifing is applied to estimate uncertainties for the ICA loadings, which also serve as a basis for significance tests. These tests are shown to improve ICA performance, indicating how many components are mixed in the observed data, and also which parts of the extracted sources that contain significant information. We address the issue of stability for the ICA model through uncertainty plots. The ICA performance is compared to principal component analysis (PCA) for two selected applications, a simulated experiment and a real world application.

Blind Source Separation and Equiprobabilistic Topographic Maps

Recently, a number of heuristic techniques have been devised in order to overcome some of the limitations of the Blind Source Separation (BSS) algorithms that are rooted in the theory of Independent Component Analysis (ICA). They are usually based on topographic maps and designed to separate mixtures of signals with either sub-Gaussian or super-Gaussian source densities. In the sub-Gaussian case, the coordinates of the winning neurons in the topographic map represent the estimates of the source signal amplitudes. In the super-Gaussian case, one relies on the topographic map's ability to detect the source directions in mixture space which, in turn, correspond to the column vectors of the mixing matrix in the linear case. We will introduce a new topographic map-based heuristic for super-Gaussian BSS. It relies on the tendency of the mixture samples to cluster around the source directions. We will demonstrate its performance on linear and mildly non-linear mixtures of speech signals, including the case where there are less mixtures than sources to be separated (“non-square” BSS).

Independent component analysis approach to resolve the multi-source limitation of the nutating rising-sun reticle based optical trackers

Independent component analysis (ICA) is described for a number of signals from different sources and a number of receivers. When applied to nutating rising-sun reticle optical trackers, ICA enables the discrimination of optical sources with an appropriate number of detectors. The main contribution of this paper is the conclusion that coherence between optical sources results in a nonlinear ICA problem that becomes linear when the optical fields are incoherent. It is shown that requirements necessary for the ICA theory to work are fulfilled for both coherent and incoherent optical sources. Moreover, it is shown additionally that by the proper design of the optical tracker the nonlinear model can be converted into linear one by simple linear bandpass filtering operation. Consequently, the multisource limitation of the nutating rising-sun reticle based optical trackers can in principle be overcome for both coherent and incoherent optical sources.

On-line blind equalization via on-line blind separation

A blind equalization method based on the theory of independent component analysis is presented. The blind equalization of an unknown FIR channel with possibly non-minimum phase is formulated as a blind separation problem. The inputs to the equalizer are formed by stacking the fractional samples of the channel outputs. New on-line blind equalization algorithms are developed from on-line blind separation algorithms, thus they inherit the equivariant property from the equivariant blind separation algorithms. Due to this property, the performance of the new algorithms is independent of the channel parameters. Therefore, they are useful for equalizing some ill-conditioned channels. It is shown by simulations that the proposed algorithm works well when the input sequence has some degree of correlation.