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Showing 1 to 15 of 15 for “"Matrix approximation"”.
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Novel Fast Algorithms For Low Rank Matrix Approximation
<p>Recent advances in matrix approximation have seen an emphasis on randomization techniques in which the goal was to create a sketch of an input matrix. This sketch, a random submatrix of an input matrix, having much fewer rows or columns, still preserves its relevant features. In one of such …
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Complex data analytics via sparse, low-rank matrix approximation
… 1) we develop Exemplar-based low-rank sparse Matrix Decomposition (EMD), a novel method for fast clustering large-scale data by incorporating low-rank approximations into matrix decomposition-based clustering; 2) we propose ECKF, a general model for large-scale Evolutionary Clustering based on …
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Sparse Kernel feature extraction
… extraction is considered for three objectives: matrix approximation, supervised feature extraction andlearning the semantics of two-viewed data. Computational and memory efficiency is prioritised, as well as sparsity in a direct manner and simple implementations. For the matrix approximation …
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Sampling-based algorithms for dimension reduction
… question, we consider the problem of low-rank matrix approximation: given a matrix A..., one wants to compute a rank-k matrix (where k << min{m, n}) nearest to A in the Frobenius norm (also known as the Hilbert-Schmidt norm). We prove that using a sample of roughly O(k/[epsilon]) rows of A one …
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Diversity-inducing probability measures for machine learning
… problems arise in machine learning within kernel approximation, experimental design, and numerous other applications. In such applications, one often seeks to select diverse subsets of items to represent the population. One way to select such diverse subsets is to sample according to …
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Ladungsanregungen im ungeordneten t-t’-t”-J-Modell
… und ist verwandt mit der sogenannten average T-matrix approximation, wird hier jedoch auf ein stark korreliertes System erweitert. Zur Illustration wird der Grundzustand von La2−xSrxCuO4 und Nd2−xCexCuO4 bei einem zusätzlichen Ladungsträger über Halbfüllung untersucht. Wie Bandstrukturrechnungen …
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Novel Monte Carlo Methods for Large-Scale Linear Algebra Operations
… linear systems, constructing low-rank matrix approximation, and approximating the extreme eigenvalues/ eigenvectors, across modern distributed and parallel computing architectures. First of all, we revisit the classical Ulam-von Neumann Monte Carlo algorithm and derive the necessary and …
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Exploring single hole states in InAs/GaAs quantum dots and quantum dot molecules under 2-D electric fields
… tight-binding atomistic simulation and a finite matrix approximation. This hybrid method can quickly explore the properties of a single hole spin state under a variety of electric field conditions. We discover that a hole spin in a single QD can be polarized in the vertical direction with an …
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Algorithmic advances in learning from large dimensional matrices and scientific data
… combining various ideas from linear algebra and approximation theory for matrix spectrum related problems such as numerical rank estimation, matrix function trace estimation including log-determinants, Schatten norms, and other spectral sums. We also propose a new method which simultaneously …
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Deconvolution and sparsity based image restoration
… (EM) approach, ii) sparse non-negative matrix approximation (SNMA), and iii) Kullback-Leibler divergence minimization (KLD). For HFW, the main objective function was split into Fourier domain deconvolution and wavelet domain denoising, to avoid the computational burden of handling …
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Deconvolution and sparsity based image restoration
… (EM) approach, ii) sparse non-negative matrix approximation (SNMA), and iii) Kullback-Leibler divergence minimization (KLD). For HFW, the main objective function was split into Fourier domain deconvolution and wavelet domain denoising, to avoid the computational burden of handling …
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Automatic Reconstruction of the Building Blocks of Molecular Interaction Networks
… of constructing closed biclusters in a binary matrix. Applying this method to a compendium of 13 stresses on human cells, we automatically detect that about four to six hours after treatment with chemicals cause endoplasmic reticulum stress, fibroblasts shut down the cell cycle far more …
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Data Clustering And Visualization Through Matrix Factorization
… is two-fold: Semi-Supervised Non-negative Matrix Factorization (SS-NMF) for data clustering/co-clustering and Exemplar-based data Visualization (EV) through matrix factorization. Compared to traditional data mining models,</p> <p>matrix-based methods are fast, easy to understand and …
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Control-Oriented Model Reduction for Parabolic Systems
… kann in einem sogenannten "quasi-dünnbesetzten“ Matrixformat, den hierarchischen (H) Matrizen, approximiert werden. Um die Problemgröße von linearen, zeitinvarianten Systemen zu reduzieren, wird sehr häufig das Verfahren des balancierten Abschneidens eingesetzt. Bei dieser Methode führt ein …
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Φασματικές μέθοδοι ανάκτησης πληροφορίας, εργαλεία λογισμικού και εφαρμογές
… υλοποιηθεί και ενταχθεί στο περιβάλλον Text to Matrix Generator (TMG). Το TMG στηρίζεται κατά κύριο λόγο στη MATLAB ενώ μικρότερα τμήματά του έχουν γραφτεί σε Perl. Το TMG αποτελείται από έξι τμήματα, ενώ είναι εύκολα επεκτάσιμο. Τα τμήματα αυτά παρέχουν μια ευρεία συλλογή μεθόδων ανάκτησης …