application of polynomial spline independent component analysis to fmri data
Application of cohesion theory in discourse analysis to the teaching of reading comprehension to foreign language learners = Áp dụng lý thuyết về liên kết văn b
... UNIVERSITY OF LANGUAGES AND INTERNATIONAL STUDIES FACULTY OF POST-GRADUATE STUDY BỒ THỊ LÝ APPLICATION OF COHESION THEORY IN DISCOURSE ANALYSIS TO THE TEACHING OF READING COMPREHENSION TO FOREIGN ... to promote personal and professional growth, to improve practice to enhance student learning, and to advance the teacher profession Data collection instruments The four main instruments for data ... mains advantages of action research namely to promote personal and professional growth, to improve practice to enhance student learning, and to advance the teacher profession 2.3 Data collection...
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báo cáo hóa học:" An application of principal component analysis to the clavicle and clavicle fixation devices" doc
... view of varying effects of principal components Dorsal view of effects of varying the first four principal components of the clavicle shape model individually Figure 10 Comparison of principal components ... shape variation Results of the principal component analysis (PCA) comprised of size and shape components A size component reflects the variation in dimensions purely due to size, with the ratios ... of each of these groups is illustrated below (Figure 11) Discussion and Conclusions The application of principal component analysis (PCA) allows the building of statistical shape models of bones...
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Independent component analysis, the validation on volume conductor platform and the application in automatic artifacts removal and source locating of egg signals
... the mean of the entries of a vector, i stands for the ith components, M stands for the number of independent components of ICA Apparently, the coefficient vector of ECG component has the minimum ... Coefficient maps of ICA components corresponding to response 60 Figure 5.21 Tomography of ICA component C6 in experiment (Pop1) reconstructed by LORETA 62 Figure 5.21 Tomography of ICA component C1 ... placement of the electrodes is shown in Fig.4.7: 40 Figure 4.7 The electrode placement scheme The EEG raw data is decomposed into a number of independent components by an Independent Component Analysis...
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Tài liệu Independent Component Analysis - Chapter 14: Overview and Comparison of Basic ICA Methods pptx
... Comparison of, say, the traces of the asymptotic variances of two estimators enables direct comparison of the accuracy of two estimators One can solve analytically for the asymptotic variance of b ... data is sphered (whitened) in a robust manner, in which case the constraint reduces to k ^ k = 1, where is the value of for whitened data Several robust estimators of the variance of ^ T or of ... COMPARISON OF BASIC ICA METHODS the error of EASI increases linearly with the number of independent components However, the error of all the algorithms is tolerable for most practical purposes Effect of...
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Báo cáo hóa học: " Blind Separation of Acoustic Signals Combining SIMO-Model-Based Independent Component Analysis and Binary Masking" doc
... yields a small distortion of less than dB Thus, the proposed idea, the use of binary masking after obtaining SIMO components of each source, is well suited to the realization of low-distortion BSS ... Frequency S1 ( f , t) component S2 ( f , t) component S1 ( f , t) component S2 ( f , t) component S1 ( f , t) component S2 ( f , t) component (a) (b) (c) Figure 4: Examples of spectra in proposed ... S1 ( f , t) component S2 ( f , t) component S1 ( f , t) component S2 ( f , t) component S1 ( f , t) component S2 ( f , t) component (a) (b) (c) Gain Gain Gain Figure 3: Examples of spectra in...
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Independent component analysis P15
... the dimension of the data In other words, we could define the vector of the independent components as ~ = (s1 ::: sk n1 ::: nl )T where the si i = ::: k are the “real” independent components and ... matrix of the noise, say , is often assumed to of the form , but this may be too restrictive in some cases In any case, the noise covariance is assumed to be known Little work on estimation of an ... that incorporates the mixing of the real ICs and the covariance structure of the noise, and the number of the independent components in ~ is equal to the number of observed mixtures Therefore,...
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Independent component analysis P16
... manner similar to the derivation of the ML or maximum a posteriori (MAP) estimator of the noise-free independent components in Chapter 15 We can write the posterior probability of as follows: ... however; due to this phenomenon of sparsity, the ML estimation is very useful In the case where the independent components are very supergaussian, most of them are very close to zero because of the ... here assumed to be infinitely small, the (t) are the realizations of the independent components, and C is an irrelevant constant The functions fi are the log-densities of the independent components...
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Independent component analysis P17
... nonlinear, because nonlinear factor analysis is able to explain the data with 10 components equally well as linear factor analysis (PCA) with 21 components Different numbers of hidden neurons and sources ... estimates of the source signals in Fig 17.3 are obtained by mapping each data vector onto the map of Fig 17.4, x 322 NONLINEAR ICA and reading the coordinates of the mapped data vector Even though ... K is the total number of gaussian components, which is equal to the number of grid points in latent space, and the prior probabilities P (i) of the gaussian components are all equal to 1=K GTM...
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Independent component analysis P18
... parameters to allow the estimation of This means that simply finding a matrix so that the components of the vector C A V z(t) = Vx(t) (18.3) are white, is not enough to estimate the independent components ... and find its maximum is to use the operator One simple way of measuring the diagonality of a matrix M) = off( X m2 = i6 j M (18.10) ij M which gives the sum of squares of the off-diagonal elements ... want to is to minimize the sum of the off-diagonal elements of several lagged covariances of = As before, we use the symmetric version y of the lagged covariance matrix Denote by S the set of...
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Independent component analysis P19
... when the original data vector is expanded to ~ , its dimension grows very much The number M of time delays that needs to be taken into account depends on the application, but it is often tens or ... each component of the vector applies the nonlinearity gi (:) to the respective component of the argument vector The optimal nonlinearity gi (:) is the negative score function gi = p0i =pi of the ... burdensome, and the number of data points needed to estimate such a large number of parameters can be prohibitive in practical applications This is especially true if we want to estimate the separating...
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Independent component analysis P20
... gives a topographic map where the distance of the components in the topographic representation is a function of the dependencies of the components Components that are near to each other in the topographic ... ICA data model, it is assumed that the components si are independent However, ICA is often applied on data sets, for example, on image data, in which the obtained estimates of the independent components ... hand, attempts to utilize the dependence of the estimated independent components to define a topographic order 20.2.1 Multidimensional ICA In multidimensional independent component analysis [66,...
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Independent component analysis P21
... spin-cycling of the distinct window algorithm The second interpretation of sliding windows is due to the method of frames Consider the case of decomposing a data vector into a linear combination of a ... were proposed to take into account some of the remaining dependencies Here we apply two of the extensions discussed in Section 20.2, independent subspace analysis and topographic ICA, to image feature ... norm of the projection onto the subspace is relatively independent of the phase of the input This is in fact what the principle of invariant-feature subspaces, one of the inspirations for independent...
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Independent component analysis P22
... corresponding to the independent components fell on the brain regions expected to be activated by the particular stimulus Applications of ICA have been proposed for analysis of other kinds of biomedical data ... Validity of the basic ICA model The application of ICA to the study of EEG and MEG signals assumes that several conditions are verified, at least approximately: the existence of statistically independent ... from MEG data, using the FastICA algorithm Three views of the field patterns generated by each independent component are plotted on top of the respective signal Full lines correspond to magnetic...
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Independent component analysis P23
... estimate of the symbol vector m from Eq (23.34) If the matrices H0 and H1 have not converged, return back to step Apply the sign nonlinearity to each component of the final estimate of the symbol vector ... denotes the noise vector consisting of subsequent C last samples of noise The vector gkl denotes the “early” part of the code vector, and gkl the “late” part, respectively These vectors are given by ... real part of the complex-valued data This allows application of ICA and BSS methods developed for real-valued data to CDMA The model (23.27) can be further expressed in the matrix-vector form...
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Independent component analysis P24
... Since the number of data points should grow with the number of parameters to obtain satisfactory estimates, it may be next to impossible to estimate the model with the small number of data points ... assumed to be roughly independent of each other Yet, depending on the policy and skills of the individual manager, e.g., advertising efforts, the effect of the factors on the cash flow of specific ... time series data The data consisted of the weekly cash flow in 40 stores that belong to the same retail chain, covering a time span of 140 weeks Some examples of the original data xi (t) are shown...
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Tài liệu Bài 1: Introduction(Independent component analysis (ICA) doc
... statistically independent This means that the value of any one of the components gives no information on the values of the other components In fact, in factor analysis it is often claimed that the factors ... information on the data as possible This leads to a family of techniques called principal component analysis or factor analysis In a classic paper, Spearman [409] considered data that consisted of school ... into account In fact, ICA could be considered as nongaussian factor analysis, since in factor analysis, we are also modeling the data as linear mixtures of some underlying factors W 1.3.2 A W Applications...
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An application of CLT principles in teaching speaking to non english majors at nghe an economics and technology college
... boring topics In fact, some of the topics in the textbook were thought to be too difficult and less interesting to the students In this case, it is the role of the teachers to modify and bring to ... students to have capability of oral communication It also purposes to make students accustomed to listening and speaking Here, the students are provided and directed to conform the outline of the story ... ability to participate successfully in oral interaction often listen in silence while others the talking One way to encourage such learners to begin to participate is to help them build up a stock of...
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Tài liệu Bài 7: What is Independent Component Analysis? docx
... What is Independent Component Analysis? In this chapter, the basic concepts of independent component analysis (ICA) are defined We start by discussing a couple of practical applications These serve ... with applications in many different areas 153 DEFINITION OF INDEPENDENT COMPONENT ANALYSIS Basically, random variables y1 y2 ::: yn are said to be independent if information on the value of yi ... give a kind of a nonrigorous, constructive proof of the identifiability 154 WHAT IS INDEPENDENT COMPONENT ANALYSIS? 7.2.3 Ambiguities of ICA In the ICA model in Eq (7.5), it is easy to see that...
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Tài liệu Independent Component Analysis - Chapter 16: ICA with Overcomplete Bases ppt
... manner similar to the derivation of the ML or maximum a posteriori (MAP) estimator of the noise-free independent components in Chapter 15 We can write the posterior probability of as follows: ... however; due to this phenomenon of sparsity, the ML estimation is very useful In the case where the independent components are very supergaussian, most of them are very close to zero because of the ... here assumed to be infinitely small, the (t) are the realizations of the independent components, and C is an irrelevant constant The functions fi are the log-densities of the independent components...
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