16 results on '"Salazar, Addisson"'
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2. Application of ICAMM to Impact-Echo Testing
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Salazar, Addisson and Salazar, Addisson
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- 2013
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3. Conclusions
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Salazar, Addisson and Salazar, Addisson
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- 2013
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4. Introduction
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Salazar, Addisson and Salazar, Addisson
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- 2013
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5. Atrial Activity Extraction Based on Statistical and Spectral Features
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Llinares, Raúl, Igual, Jorge, Salazar, Addisson, Miró-Borrás, Julio, Serrano, Arturo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Adali, Tülay, editor, Jutten, Christian, editor, Romano, João Marcos Travassos, editor, and Barros, Allan Kardec, editor
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- 2009
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6. Two Applications of Independent Component Analysis for Non-destructive Evaluation by Ultrasounds
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Salazar, Addisson, Gosálbez, Jorge, Igual, Jorge, Llinares, Raul, Vergara, Luis, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Rosca, Justinian, editor, Erdogmus, Deniz, editor, Príncipe, José C., editor, and Haykin, Simon, editor
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- 2006
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7. ICA Model Applied to Multichannel Non-destructive Evaluation by Impact-Echo
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Salazar, Addisson, Vergara, Luis, Igual, Jorge, Gosálbez, Jorge, Miralles, Ramón, Puntonet, Carlos G., editor, and Prieto, Alberto, editor
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- 2004
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8. Independent Component Analysis (ICA): Algorithms, Applications and Ambiguities
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Salazar, Addisson, Vergara, Luis, Salazar, Addisson, and Vergara, Luis
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- Independent component analysis
- Abstract
Modern treatment of data requires powerful tools that allow the possible valuable contents of that data to be thoroughly understood and exploited. From the plethora of techniques proposed to achieve those objectives, the independent component analysis (ICA) has emerged as a flexible and efficient approach to model and characterize arbitrary data densities. Considering adequate data preprocessing, ICA can be implemented for any kind of data including imaging; biomedical signals; telecommunication data; and web data. In this framework, this book embraces a significant vision of ICA that presents innovative theoretical and practical approaches. ICA has been increasingly studied as a suitable method for many applications where available data describe complex geometries. Thus, this book aims to be an updated and advanced source of knowledge to solve real-world problems efficiently based on ICA. In contrast to classical time and frequency domain filtering, ICA has been proposed as a statistical filtering tool considering the observed data as mixtures of hidden non-Gaussian distributions called sources. Those sources extracted by ICA can be related with meaningful information about the origin of the data and for data detection/classification. Therefore, the successful of ICA has been widely demonstrated in challenging blind source separation (BSS), feature extraction, and pattern recognition tasks. The suitability of ICA for a given problem of data analysis can be posed from different perspectives considering the physical interpretation of the phenomenon under analysis: (i) Estimation of the probability density of multivariate data without physical meaning; (ii) learning of some bases (usually called activation functions), which are more or less connected to the actual behaviors that are implicit in the physical phenomenon; and (iii) to identify where sources are originated and how they mix before arriving to the sensors to provide a physical explanation of the linear mixture model. In any case, even though the complexity of the problem constrains a physical interpretation, ICA can be used as a general-purpose data mining technique. The chapters that compose this book are written by premier researchers that present enlightening discussions, convincing demonstrations, and guidelines for future directions of research. The contents of this book span biomedical signal processing, dynamic modeling, next generation wireless communication, and sound and ultrasound signal processing. It also includes comprehensive works based on the related ICA techniques known as bounded component analysis (BCA) and non-negative matrix factorization (NMF).
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- 2017
9. Nonlinear estimators from ICA mixture models.
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Safont, Gonzalo, Salazar, Addisson, Vergara, Luis, and Rodríguez, Alberto
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NONLINEAR analysis , *INDEPENDENT component analysis , *ELECTROENCEPHALOGRAPHY , *IMAGE reconstruction , *COMPUTER algorithms , *MATHEMATICAL models - Abstract
Abstract Independent Component Analyzers Mixture Models (ICAMM) are versatile and general models for a large variety of probability density functions. In this paper we assume ICAMM to derive new MAP and LMSE estimators. The first one (MAP-ICAMM) is obtained by an iterative gradient algorithm, while the second (LMSE-ICAMM) admits a closed-form solution. Both estimators can be combined by using LMSE-ICAMM to initialize the iterative computation of MAP-ICAMM.The new estimators are applied to the reconstruction of missed channels in EEG multichannel analysis. The experiments demonstrate the superiority of the new estimators with respect to: Spherical Splines, Hermite, Partial Least Squares, Support Vector Regression, and Random Forest Regression. [ABSTRACT FROM AUTHOR]
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- 2019
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10. Probabilistic Distance for Mixtures of Independent Component Analyzers.
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Safont, Gonzalo, Salazar, Addisson, Vergara, Luis, Gomez, Enriqueta, and Villanueva, Vicente
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INDEPENDENT component analysis , *ARTIFICIAL neural networks , *ELECTROENCEPHALOGRAPHY , *NEUROPSYCHOLOGICAL tests , *PROBABILITY density function - Abstract
Independent component analysis (ICA) is a blind source separation technique where data are modeled as linear combinations of several independent non-Gaussian sources. The independence and linear restrictions are relaxed using several ICA mixture models (ICAMMs) obtaining a two-layer artificial neural network structure. This allows for dependence between sources of different classes, and thus, a myriad of multidimensional probability density functions can be accurate modeled. This paper proposes a new probabilistic distance (PDI) between the parameters learned for two ICAMMs. The PDI is computed explicitly, unlike the popular Kullback–Leibler divergence (KLD) and other similar metrics, removing the need for numerical integration. Furthermore, the PDI is symmetric and bounded within 0 and 1, which enables its use as a posterior probability in fusion approaches. In this paper, the PDI is employed for change detection by measuring the distance between two ICAMMs learned in consecutive time windows. The changes might be associated with relevant states from a process under analysis that are explicitly reflected in the learned ICAMM parameters. The proposed distance was tested in two challenging applications using simulated and real data: 1) detecting flaws in materials using ultrasounds and 2) detecting changes in electroencephalography signals from humans performing neuropsychological tests. The results demonstrate that the PDI outperforms the KLD in change-detection capabilities. [ABSTRACT FROM PUBLISHER]
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- 2018
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11. On Recovering Missing Ground Penetrating Radar Traces by Statistical Interpolation Methods.
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Safont, Gonzalo, Salazar, Addisson, Rodriguez, Alberto, and Vergara, Luis
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INTERPOLATION , *GROUND penetrating radar , *REMOTE sensing , *MISSING data (Statistics) , *PROBABILITY theory - Abstract
Missing traces in ground penetrating radar (GPR) B-scans (radargrams) may appear because of limited scanning resolution, failures during the acquisition process or the lack of accessibility to some areas under test. Four statistical interpolation methods for recovering these missing traces are compared in this paper: Kriging, Wiener structures, Splines and the expectation assuming an independent component analyzers mixture model (E-ICAMM). Kriging is an adaptation to the spatial context of the linear least mean squared error estimator. Wiener structures improve the linear estimator by including a nonlinear scalar function. Splines are a commonly used method to interpolate GPR traces. This consists of piecewise-defined polynomial curves that are smooth at the connections (or knots) between pieces. E-ICAMM is a new method proposed in this paper. E-ICAMM consists of computing the optimum nonlinear estimator (the conditional mean) assuming a non-Gaussian mixture model for the joint probability density in the observation space. The proposed methods were tested on a set of simulated data and a set of real data, and four performance indicators were computed. Real data were obtained by GPR inspection of two replicas of historical walls. Results show the superiority of E-ICAMM in comparison with the other three methods in the application of reconstructing incomplete B-scans. [ABSTRACT FROM AUTHOR]
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- 2014
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12. Learning material defect patterns by separating mixtures of independent component analyzers from NDT sonic signals
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Salazar, Addisson, Vergara, Luis, and Llinares, Raúl
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FAULT location (Engineering) , *INDEPENDENT component analysis , *MATERIAL falsity , *NONDESTRUCTIVE testing , *DISCRIMINANT analysis , *IMPACT testing , *FINITE element method , *MASS spectrometry , *SIGNAL processing - Abstract
Abstract: This paper introduces the application of independent component analysis mixture modelling (ICAMM) in non-destructive testing (NDT). The application consists of discriminating patterns for material quality control from homogeneous and defective materials inspected by impact-echo testing. This problem is modelled as a mixture of independent component analysis (ICA) models, representing a class of defective or homogeneous material by an ICA model whose parameters are learned from the impact-echo signal spectrum. These parameters define a kind of particular signature for the different defects. The proposed procedure is intended to exploit to the maximum the information obtained with the cost efficiency of only a single impact. To illustrate this capability, four levels of classification detail (material condition, kind of defect, defect orientation, and defect dimension) are defined, with the lowest level of detail having up to 12 classes. The results from several 3D finite element models and lab specimens of an aluminium alloy that contain defects of different shapes and sizes in different locations are included. The performance of the classification by ICA mixtures is compared with linear discriminant analysis (LDA) and with multi-layer perceptron (MLP) classification. We demonstrate that the mass spectra from impact-echo testing fit ICAMM, and we also show the feasibility of ICAMM to contribute in NDT applications. [Copyright &y& Elsevier]
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- 2010
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13. A general procedure for learning mixtures of independent component analyzers
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Salazar, Addisson, Vergara, Luis, Serrano, Arturo, and Igual, Jorge
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INDEPENDENT component analysis , *FINITE element method , *NONPARAMETRIC statistics , *SUPERVISED learning , *MIXTURE distributions (Probability theory) , *NONDESTRUCTIVE testing , *DATA analysis , *BLIND source separation - Abstract
Abstract: This paper presents a new procedure for learning mixtures of independent component analyzers. The procedure includes non-parametric estimation of the source densities, supervised–unsupervised learning of the model parameters, incorporation of any independent component analysis (ICA) algorithm into the learning of the ICA mixtures, and estimation of residual dependencies after training for correction of the posterior probability of every class to the testing observation vector. We demonstrate the performance of the procedure in the classification of ICA mixtures of two, three, and four classes of synthetic data, and in the classification of defective materials, consisting of 3D finite element models and lab specimens, in non-destructive testing using the impact-echo technique. The application of the proposed posterior probability correction demonstrates an improvement in the classification accuracy. Semi-supervised learning shows that unlabeled data can degrade the performance of the classifier when they do not fit the generative model. Comparative results of the proposed method and standard ICA algorithms for blind source separation in one and multiple ICA data mixtures show the suitability of the non-parametric ICA mixture-based method for data modeling. [Copyright &y& Elsevier]
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- 2010
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14. ICA Mixtures Applied to Ultrasonic Nondestructive Classification of Archaeological Ceramics.
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Salazar, Addisson and Vergara, Luis
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INDEPENDENT component analysis ,PROBABILITY theory ,AD hoc computer networks ,VECTOR spaces ,CERAMICS ,ARCHAEOLOGY equipment ,NONDESTRUCTIVE testing ,ARCHAEOLOGICAL dating ,SIGNAL processing ,GAUSSIAN distribution - Abstract
We consider a classifier based on Independent Component Analysis Mixture Modelling (ICAMM) to model the feature jointprobability density. This classifier is applied to a challenging novel application: classification of archaeological ceramics. ICAMM gathers relevant characteristics that have general interest for material classification. It can deal with arbitrary forms of the underlying probability densities in the feature vector space as nonparametric methods can do. Mutual dependences among the features are modelled in a parametric form so that ICAMM can achieve good performance even with a training set of relatively small size, which is characteristic of parametric methods. Moreover, in the training stage, ICAMM can incorporate probabilistic semisupervision (PSS): labelling by an expert of a portion of the whole available training set of samples. These properties of ICAMM are well-suited for the problem considered: classification of ceramic pieces coming from four different periods, namely, Bronze Age, Iberian, Roman, and Middle Ages. A feature set is obtained from the processing of the ultrasonic signal that is recorded in through-transmission mode using an ad hoc device. A physical explanation of the results is obtained with comparison with classical methods used in archaeology. The results obtained demonstrate the promising potential of ICAMM for material classification. [ABSTRACT FROM AUTHOR]
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- 2010
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15. Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering.
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Prades, José, Safont, Gonzalo, Salazar, Addisson, and Vergara, Luis
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HYPERSPECTRAL imaging systems ,SPECTRAL imaging ,INDEPENDENT component analysis ,ALGORITHMS ,K-means clustering - Abstract
Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a hyperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agglomerative clustering. The algorithm is based on the assumption that a valid clustering of the image has one cluster for each different material. After reducing the dimensionality of the hyperspectral image, the proposed method obtains an initial clustering using K-means. In this stage, cluster densities are estimated using Independent Component Analysis. Based on the K-means result, a model-based agglomerative clustering is performed, which provides a hierarchy of clusterings. Finally, a validation algorithm selects a clustering of the hierarchy; the number of clusters it contains is the estimated number of materials. Besides estimating the number of endmembers, the proposed method can approximately obtain the endmember (or spectrum) of each material by computing the centroid of its corresponding cluster. We have tested the proposed method using several hyperspectral images. The results show that the proposed method obtains approximately the number of materials that these images contain. [ABSTRACT FROM AUTHOR]
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- 2020
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16. Computing the Partial Correlation of ICA Models for Non-Gaussian Graph Signal Processing.
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Belda, Jordi, Vergara, Luis, Safont, Gonzalo, and Salazar, Addisson
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GRAPH theory ,SIGNAL processing ,STATISTICAL correlation ,NONLINEAR estimation ,INDEPENDENT component analysis - Abstract
Conventional partial correlation coefficients (PCC) were extended to the non-Gaussian case, in particular to independent component analysis (ICA) models of the observed multivariate samples. Thus, the usual methods that define the pairwise connections of a graph from the precision matrix were correspondingly extended. The basic concept involved replacing the implicit linear estimation of conventional PCC with a nonlinear estimation (conditional mean) assuming ICA. Thus, it is better eliminated the correlation between a given pair of nodes induced by the rest of nodes, and hence the specific connectivity weights can be better estimated. Some synthetic and real data examples illustrate the approach in a graph signal processing context. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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