156,283 research outputs found
Automatic classification of lithofacies using fast independent component analysis
The problem of automatic classification of facies was addressed using the Fast Independent Component Analysis (FastICA) of a data set of geophysical well logs of the Namorado Field, Campos Basin, Brazil, followed by a k-nearest neighbor (k-NN) classification. The goal of an automatic classification of facies is to produce spatial models of facies that assist the geological characterization of petroleum reservoirs. The FastICA technique provides a new data set that has the most stable and less Gaussian distribution possible. The k-NN classifies this new data set according to its characteristics. The previous application of FastICA improves the accuracy of the k-NN automatic classification and it also provides better results in comparison with the automatic classification by means of the Principal Component Analysis (PCA). © 2015 Sociedade Brasileira de GeofĂsica.The problem of automatic classification of facies was addressed using the Fast Independent Component Analysis (FastICA) of a data set of geophysical well logs of the Namorado Field, Campos Basin, Brazil, followed by a k-nearest neighbor (k-NN) classification. The goal of an automatic classification of facies is to produce spatial models of facies that assist the geological characterization of petroleum reservoirs. The FastICA technique provides a new data set that has the most stable and less Gaussian distribution possible. The k-NN classifies this new data set according to its characteristics. The previous application of FastICA improves the accuracy of the k-NN automatic classification and it also provides better results in comparison with the automatic classification by means of the Principal Component Analysis (PCA)331119126Barboza, E.G., (2005) Análise Estratigráfica Do Campo De Namorado Com Base Na interpretação SĂsmica Tridimensional, 230p. , Doctorate Thesis – UFRGS, BrazilCardoso, J.F., Souloumiac, A., Blind beamforming for non Gaussian signals (1993) IEE Proceedings-F, 140 (6), pp. 362-370Casey, M.A., Method for extracting features from a mixture of signals (2001) Mitsubishi Electric Research Laboratories, , Inc., Cambridge, MA, U.S. Patent n. 6,321,200Comon, P., Independent Component Analysis: A new concept? (1994) Signal Processing, 36 (3), pp. 287-314Cover, T.M., Hart, P.E., Nearest neighbor pattern classification (1967) IEEE Transactions on Information Theory, 13 (1), pp. 21-27Dasarathy, B.V., (1991) Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques, pp. 211-215. , IEEE Computer Society Press, Los Alamitos, Calif. WashingtonFlexa, R.T., Rade, A., Carrasquilla, A., Identificação de litotipos nos perfis de poço do Campo de Namorado (Bacia de Campos, Brasil) e do Lago Maracaibo (Venezuela) aplicando estatĂstica multivariada (2004) Revista Brasileira De GeociĂŞncias, 34 (4), pp. 571-578Françamm, Análise do uso da terra no municĂpio de Viçosa-MG mediado por classificações supervisionadas com Redes Neurais Artificiais e Maxver (2009) Revista Brasileira De Geografia FĂsica, 2 (3), pp. 92-101Giannakopoulos, X., Karhunen, J., Oja, E., A Comparison of neural ICA algorithms using Real-world data (1999) International Joint Conference on Neural Networks, 2, pp. 888-893. , Washington, DCHyvärinen, A., Fast and Robust Fixed-Point Algorithms for IndependentComponentAnalysis (1999) IEEE Transactions on Neural Networks, 10 (3), pp. 626-634Hyvärinen, A., Oja, E., A Fast fixed-point algorithm for Independent Component Analysis (1997) Neural Computation, 9 (7), pp. 1483-1492Hyvärinen, A., Karhunen, J., Oja, E., (2001) Independent Component Analysis, 504p. , Wiley and Sons, New YorkKuhn, H.W., Tucker, A.W., Nonlinear programming (1951) Proceedings of Berkeleysymposium, 2, pp. 481-492. , Berkeley: University of California PressLeite, E.P., Souza Filho, C.R., TEXTNN – A MATLAB program for textural classification using neural networks (2009) Computers& Geosciences, 35 (10), pp. 2084-2094Macleod, A.J., A generalization of Newton-Raphson (1984) Int. J. Math. Educ. Sci. Technol., 15 (1), pp. 117-120Macqueen, J.B., Some Methods for classification and Analysis of Multivariate Observations (1967) Proceedings of Berkeley Symposium on Mathematical Statistics and Probability, 1, pp. 281-297. , 5., Berkeley, University of California PressMarchini, J.L., Heaton, C., Ripley, B.D., (2009) Fastica Algorithms to Perform ICA and Projection Pursuit, 1, pp. 1-11. , R package versionMurata, N., Ikeda, S., Ziehe, A., An approach to blind source separation based on temporal structure of speech signals (2001) Neurocomputing, 41, pp. 1-24Rosa, H., Suslick, S.B., Vidal, A.C., Sakai, G.K., Caracterização de eletrofácies por meio de ferramentas estatĂsticas multivariadas (2008) Rem: Rev. Esc. Minas, 61 (4), pp. 415-422Sancevero, S.S., Remacre, A.Z., Vidal, A.C., Portugal, R.S., Aplicação de tĂ©cnicas de estatĂstica multivariada na definição da litologia a partir de perfis geofĂsicos de poços (2008) Revista Brasileira De GeociĂŞncias, 38 (1), pp. 61-74Toussaint, G.T., Geometric proximity graphs for improving nearest neighbor methods in instance-based learning and data mining (2005) International Journal of Computational Geometry and Applications, 15 (2), pp. 101-150Vidal, A.C., Sancevero, S.S., Remacre, A.Z., Costanzo, C.P., Modelagem GeoestatĂstica 3D da Impedância AcĂşstica para a Caracterização do Campo de Namorado (2007) Brazilian Journal of Geophysics, 25 (3), pp. 295-305Zhang, K., Chan, L., Dimension reduction as a deflation method in ICA (2006) Signal Processing Letters, IEEE, 13 (1), pp. 45-4
Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms
Brain networks in fMRI are typically identified using spatial independent
component analysis (ICA), yet mathematical constraints such as sparse coding
and positivity both provide alternate biologically-plausible frameworks for
generating brain networks. Non-negative Matrix Factorization (NMF) would
suppress negative BOLD signal by enforcing positivity. Spatial sparse coding
algorithms ( Regularized Learning and K-SVD) would impose local
specialization and a discouragement of multitasking, where the total observed
activity in a single voxel originates from a restricted number of possible
brain networks.
The assumptions of independence, positivity, and sparsity to encode
task-related brain networks are compared; the resulting brain networks for
different constraints are used as basis functions to encode the observed
functional activity at a given time point. These encodings are decoded using
machine learning to compare both the algorithms and their assumptions, using
the time series weights to predict whether a subject is viewing a video,
listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects.
For classifying cognitive activity, the sparse coding algorithm of
Regularized Learning consistently outperformed 4 variations of ICA across
different numbers of networks and noise levels (p0.001). The NMF algorithms,
which suppressed negative BOLD signal, had the poorest accuracy. Within each
algorithm, encodings using sparser spatial networks (containing more
zero-valued voxels) had higher classification accuracy (p0.001). The success
of sparse coding algorithms may suggest that algorithms which enforce sparse
coding, discourage multitasking, and promote local specialization may capture
better the underlying source processes than those which allow inexhaustible
local processes such as ICA
BMICA-independent component analysis based on B-spline mutual information estimator
The information theoretic concept of mutual information provides a general framework to evaluate dependencies between variables. Its estimation however using B-Spline has not been used before in creating an approach for Independent Component Analysis. In this paper we present a B-Spline estimator for mutual information to find the independent components in mixed signals. Tested using electroencephalography (EEG) signals the resulting BMICA (B-Spline Mutual Information Independent Component Analysis)
exhibits better performance than the standard Independent Component Analysis algorithms of FastICA, JADE, SOBI and EFICA in similar simulations. BMICA was found to be also more reliable than the 'renown' FastICA
New Negentropy Optimization Schemes for Blind Signal Extraction of Complex Valued Sources
Blind signal extraction, a hot issue in the field of communication signal processing, aims to retrieve the sources through the optimization of contrast functions. Many contrasts based on higher-order statistics such as kurtosis, usually behave sensitive to outliers. Thus, to achieve robust results, nonlinear functions are utilized as contrasts to approximate the negentropy criterion, which is also a classical metric for non-Gaussianity. However, existing methods generally have a high computational cost, hence leading us to address the problem of efficient optimization of contrast function. More precisely, we design a novel “reference-based” contrast function based on negentropy approximations, and then propose a new family of algorithms (Alg.1 and Alg.2) to maximize it. Simulations confirm the convergence of our method to a separating solution, which is also analyzed in theory. We also validate the theoretic complexity analysis that Alg.2 has a much lower computational cost than Alg.1 and existing optimization methods based on negentropy criterion. Finally, experiments for the separation of single sideband signals illustrate that our method has good prospects in real-world applications
A Nonconvex Projection Method for Robust PCA
Robust principal component analysis (RPCA) is a well-studied problem with the
goal of decomposing a matrix into the sum of low-rank and sparse components. In
this paper, we propose a nonconvex feasibility reformulation of RPCA problem
and apply an alternating projection method to solve it. To the best of our
knowledge, we are the first to propose a method that solves RPCA problem
without considering any objective function, convex relaxation, or surrogate
convex constraints. We demonstrate through extensive numerical experiments on a
variety of applications, including shadow removal, background estimation, face
detection, and galaxy evolution, that our approach matches and often
significantly outperforms current state-of-the-art in various ways.Comment: In the proceedings of Thirty-Third AAAI Conference on Artificial
Intelligence (AAAI-19
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