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svd algorithm  (MathWorks Inc)


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    Structured Review

    MathWorks Inc svd algorithm
    Svd Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/svd-algorithm/10__1016_slash_j__cam__2024__115990-185-11-10
    Average 90 stars, based on 1 article reviews
    svd algorithm - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    Activity Assay:

    Article Title: Influence of Binaural Beats Stimulation of Gamma Frequency over Memory Performance and EEG Spectral Density
    Article Snippet: We performed the Principal Component Analysis (PCA) to reduce dimensionality using the algorithm of Singular Value Decomposition (SVD), the default mode of the Matlab function pca() .

    Article Title: Bending analysis of quasicrystal plates using adaptive radial basis function method
    Article Snippet: A novel mesh-free kernel method is presented for solving function interpolation problems and partial differential equations (PDEs).. Despite the simplicity and accuracy of the radial basis function (RBF) method, two main difficulties have been identified: the selection of the scale or shape parameter and the ill-conditioning issue.. To address these challenges, an effective condition number (ECN) strategy is proposed for the scale parameter selection, utilizing the ill-conditioning issue to minimize errors.

    Article Title: Aerodynamic noise reduction of high-speed pantograph by introducing planar jet on leeward surface of panhead
    Article Snippet: Combining the improved delayed detached eddy simulation (IDDES) model and Ffowcs Williams-Hawkings (FW–H) equation, the potential of panhead leeward-side jet in pantograph aerodynamic noise control is numerically investigated.. A typical double-strip pantograph is used as the baseline model. Two identical planar jets are arranged on the leeward side of the two strips to achieve active flow control.. The results show that the planar jet can reduce the aerodynamic drag of the upstream strip and suppress its lift fluctuation, while the jet does not produce equivalent level of positive effects on the downstream strip.

    Article Title: Heterogeneous spatial tuning in the auditory pathway of the Mongolian Gerbil (Meriones unguiculatus).
    Article Snippet: Per nucleus, we normalized the RAFs to the maximum spike rate elicited by the loudest level tested and subtracted the mean across all normalized RAFs before performing a singular value decomposition (Matlab function: pca with ‘svd’ algorithm option).

    Article Title: Post-ischemic reorganization of sensory responses in cerebral cortex
    Article Snippet: We first applied principal components analysis (PCA; MATLAB R2017a + ‘pca’ function with ‘Algorithm’ parameter set to ‘svd’) to qualitatively describe the different types of evoked responses for each condition, applying a singular value decomposition to the mean channel spike rates separately for each stimulus type; then, using the groupings for which the same basis subspace could accurately reconstruct the original observations, we seeded a reconstructed-independent components analysis algorithm (r-ICA; MATLAB R2017a + ‘rica’ function from the Statistics and Machine Learning Toolbox) using the top-3 combined-basis eigenvectors to recover a basis for the sets of components described above ( ).

    Article Title: Mutation in the Common Docking Domain Affects MAP Kinase ERK2 Catalysis and Stability
    Article Snippet: Far-UV CD spectra recorded at an increasing GdmCl concentration were analyzed by a singular value decomposition algorithm (SVD) using the software MATLAB (Math-Works, South Natick, MA, USA) to remove the high frequency noise and the low frequency random errors, and to determine the number of independent components in any given set of spectra, as described in [ ].

    Blocking Assay:

    Article Title: Influence of Binaural Beats Stimulation of Gamma Frequency over Memory Performance and EEG Spectral Density
    Article Snippet: We performed the Principal Component Analysis (PCA) to reduce dimensionality using the algorithm of Singular Value Decomposition (SVD), the default mode of the Matlab function pca() .

    Article Title: Bending analysis of quasicrystal plates using adaptive radial basis function method
    Article Snippet: A novel mesh-free kernel method is presented for solving function interpolation problems and partial differential equations (PDEs).. Despite the simplicity and accuracy of the radial basis function (RBF) method, two main difficulties have been identified: the selection of the scale or shape parameter and the ill-conditioning issue.. To address these challenges, an effective condition number (ECN) strategy is proposed for the scale parameter selection, utilizing the ill-conditioning issue to minimize errors.

    Article Title: Aerodynamic noise reduction of high-speed pantograph by introducing planar jet on leeward surface of panhead
    Article Snippet: Combining the improved delayed detached eddy simulation (IDDES) model and Ffowcs Williams-Hawkings (FW–H) equation, the potential of panhead leeward-side jet in pantograph aerodynamic noise control is numerically investigated.. A typical double-strip pantograph is used as the baseline model. Two identical planar jets are arranged on the leeward side of the two strips to achieve active flow control.. The results show that the planar jet can reduce the aerodynamic drag of the upstream strip and suppress its lift fluctuation, while the jet does not produce equivalent level of positive effects on the downstream strip.

    Article Title: Heterogeneous spatial tuning in the auditory pathway of the Mongolian Gerbil (Meriones unguiculatus).
    Article Snippet: Per nucleus, we normalized the RAFs to the maximum spike rate elicited by the loudest level tested and subtracted the mean across all normalized RAFs before performing a singular value decomposition (Matlab function: pca with ‘svd’ algorithm option).

    Article Title: Post-ischemic reorganization of sensory responses in cerebral cortex
    Article Snippet: We first applied principal components analysis (PCA; MATLAB R2017a + ‘pca’ function with ‘Algorithm’ parameter set to ‘svd’) to qualitatively describe the different types of evoked responses for each condition, applying a singular value decomposition to the mean channel spike rates separately for each stimulus type; then, using the groupings for which the same basis subspace could accurately reconstruct the original observations, we seeded a reconstructed-independent components analysis algorithm (r-ICA; MATLAB R2017a + ‘rica’ function from the Statistics and Machine Learning Toolbox) using the top-3 combined-basis eigenvectors to recover a basis for the sets of components described above ( ).

    Article Title: Mutation in the Common Docking Domain Affects MAP Kinase ERK2 Catalysis and Stability
    Article Snippet: Far-UV CD spectra recorded at an increasing GdmCl concentration were analyzed by a singular value decomposition algorithm (SVD) using the software MATLAB (Math-Works, South Natick, MA, USA) to remove the high frequency noise and the low frequency random errors, and to determine the number of independent components in any given set of spectra, as described in [ ].

    Circular Dichroism:

    Article Title: Influence of Binaural Beats Stimulation of Gamma Frequency over Memory Performance and EEG Spectral Density
    Article Snippet: We performed the Principal Component Analysis (PCA) to reduce dimensionality using the algorithm of Singular Value Decomposition (SVD), the default mode of the Matlab function pca() .

    Article Title: Bending analysis of quasicrystal plates using adaptive radial basis function method
    Article Snippet: A novel mesh-free kernel method is presented for solving function interpolation problems and partial differential equations (PDEs).. Despite the simplicity and accuracy of the radial basis function (RBF) method, two main difficulties have been identified: the selection of the scale or shape parameter and the ill-conditioning issue.. To address these challenges, an effective condition number (ECN) strategy is proposed for the scale parameter selection, utilizing the ill-conditioning issue to minimize errors.

    Article Title: Aerodynamic noise reduction of high-speed pantograph by introducing planar jet on leeward surface of panhead
    Article Snippet: Combining the improved delayed detached eddy simulation (IDDES) model and Ffowcs Williams-Hawkings (FW–H) equation, the potential of panhead leeward-side jet in pantograph aerodynamic noise control is numerically investigated.. A typical double-strip pantograph is used as the baseline model. Two identical planar jets are arranged on the leeward side of the two strips to achieve active flow control.. The results show that the planar jet can reduce the aerodynamic drag of the upstream strip and suppress its lift fluctuation, while the jet does not produce equivalent level of positive effects on the downstream strip.

    Article Title: Heterogeneous spatial tuning in the auditory pathway of the Mongolian Gerbil (Meriones unguiculatus).
    Article Snippet: Per nucleus, we normalized the RAFs to the maximum spike rate elicited by the loudest level tested and subtracted the mean across all normalized RAFs before performing a singular value decomposition (Matlab function: pca with ‘svd’ algorithm option).

    Article Title: Post-ischemic reorganization of sensory responses in cerebral cortex
    Article Snippet: We first applied principal components analysis (PCA; MATLAB R2017a + ‘pca’ function with ‘Algorithm’ parameter set to ‘svd’) to qualitatively describe the different types of evoked responses for each condition, applying a singular value decomposition to the mean channel spike rates separately for each stimulus type; then, using the groupings for which the same basis subspace could accurately reconstruct the original observations, we seeded a reconstructed-independent components analysis algorithm (r-ICA; MATLAB R2017a + ‘rica’ function from the Statistics and Machine Learning Toolbox) using the top-3 combined-basis eigenvectors to recover a basis for the sets of components described above ( ).

    Article Title: Mutation in the Common Docking Domain Affects MAP Kinase ERK2 Catalysis and Stability
    Article Snippet: Far-UV CD spectra recorded at an increasing GdmCl concentration were analyzed by a singular value decomposition algorithm (SVD) using the software MATLAB (Math-Works, South Natick, MA, USA) to remove the high frequency noise and the low frequency random errors, and to determine the number of independent components in any given set of spectra, as described in [ ].

    Concentration Assay:

    Article Title: Influence of Binaural Beats Stimulation of Gamma Frequency over Memory Performance and EEG Spectral Density
    Article Snippet: We performed the Principal Component Analysis (PCA) to reduce dimensionality using the algorithm of Singular Value Decomposition (SVD), the default mode of the Matlab function pca() .

    Article Title: Bending analysis of quasicrystal plates using adaptive radial basis function method
    Article Snippet: A novel mesh-free kernel method is presented for solving function interpolation problems and partial differential equations (PDEs).. Despite the simplicity and accuracy of the radial basis function (RBF) method, two main difficulties have been identified: the selection of the scale or shape parameter and the ill-conditioning issue.. To address these challenges, an effective condition number (ECN) strategy is proposed for the scale parameter selection, utilizing the ill-conditioning issue to minimize errors.

    Article Title: Aerodynamic noise reduction of high-speed pantograph by introducing planar jet on leeward surface of panhead
    Article Snippet: Combining the improved delayed detached eddy simulation (IDDES) model and Ffowcs Williams-Hawkings (FW–H) equation, the potential of panhead leeward-side jet in pantograph aerodynamic noise control is numerically investigated.. A typical double-strip pantograph is used as the baseline model. Two identical planar jets are arranged on the leeward side of the two strips to achieve active flow control.. The results show that the planar jet can reduce the aerodynamic drag of the upstream strip and suppress its lift fluctuation, while the jet does not produce equivalent level of positive effects on the downstream strip.

    Article Title: Heterogeneous spatial tuning in the auditory pathway of the Mongolian Gerbil (Meriones unguiculatus).
    Article Snippet: Per nucleus, we normalized the RAFs to the maximum spike rate elicited by the loudest level tested and subtracted the mean across all normalized RAFs before performing a singular value decomposition (Matlab function: pca with ‘svd’ algorithm option).

    Article Title: Post-ischemic reorganization of sensory responses in cerebral cortex
    Article Snippet: We first applied principal components analysis (PCA; MATLAB R2017a + ‘pca’ function with ‘Algorithm’ parameter set to ‘svd’) to qualitatively describe the different types of evoked responses for each condition, applying a singular value decomposition to the mean channel spike rates separately for each stimulus type; then, using the groupings for which the same basis subspace could accurately reconstruct the original observations, we seeded a reconstructed-independent components analysis algorithm (r-ICA; MATLAB R2017a + ‘rica’ function from the Statistics and Machine Learning Toolbox) using the top-3 combined-basis eigenvectors to recover a basis for the sets of components described above ( ).

    Article Title: Mutation in the Common Docking Domain Affects MAP Kinase ERK2 Catalysis and Stability
    Article Snippet: Far-UV CD spectra recorded at an increasing GdmCl concentration were analyzed by a singular value decomposition algorithm (SVD) using the software MATLAB (Math-Works, South Natick, MA, USA) to remove the high frequency noise and the low frequency random errors, and to determine the number of independent components in any given set of spectra, as described in [ ].

    Software:

    Article Title: Influence of Binaural Beats Stimulation of Gamma Frequency over Memory Performance and EEG Spectral Density
    Article Snippet: We performed the Principal Component Analysis (PCA) to reduce dimensionality using the algorithm of Singular Value Decomposition (SVD), the default mode of the Matlab function pca() .

    Article Title: Bending analysis of quasicrystal plates using adaptive radial basis function method
    Article Snippet: A novel mesh-free kernel method is presented for solving function interpolation problems and partial differential equations (PDEs).. Despite the simplicity and accuracy of the radial basis function (RBF) method, two main difficulties have been identified: the selection of the scale or shape parameter and the ill-conditioning issue.. To address these challenges, an effective condition number (ECN) strategy is proposed for the scale parameter selection, utilizing the ill-conditioning issue to minimize errors.

    Article Title: Aerodynamic noise reduction of high-speed pantograph by introducing planar jet on leeward surface of panhead
    Article Snippet: Combining the improved delayed detached eddy simulation (IDDES) model and Ffowcs Williams-Hawkings (FW–H) equation, the potential of panhead leeward-side jet in pantograph aerodynamic noise control is numerically investigated.. A typical double-strip pantograph is used as the baseline model. Two identical planar jets are arranged on the leeward side of the two strips to achieve active flow control.. The results show that the planar jet can reduce the aerodynamic drag of the upstream strip and suppress its lift fluctuation, while the jet does not produce equivalent level of positive effects on the downstream strip.

    Article Title: Heterogeneous spatial tuning in the auditory pathway of the Mongolian Gerbil (Meriones unguiculatus).
    Article Snippet: Per nucleus, we normalized the RAFs to the maximum spike rate elicited by the loudest level tested and subtracted the mean across all normalized RAFs before performing a singular value decomposition (Matlab function: pca with ‘svd’ algorithm option).

    Article Title: Post-ischemic reorganization of sensory responses in cerebral cortex
    Article Snippet: We first applied principal components analysis (PCA; MATLAB R2017a + ‘pca’ function with ‘Algorithm’ parameter set to ‘svd’) to qualitatively describe the different types of evoked responses for each condition, applying a singular value decomposition to the mean channel spike rates separately for each stimulus type; then, using the groupings for which the same basis subspace could accurately reconstruct the original observations, we seeded a reconstructed-independent components analysis algorithm (r-ICA; MATLAB R2017a + ‘rica’ function from the Statistics and Machine Learning Toolbox) using the top-3 combined-basis eigenvectors to recover a basis for the sets of components described above ( ).

    Article Title: Mutation in the Common Docking Domain Affects MAP Kinase ERK2 Catalysis and Stability
    Article Snippet: Far-UV CD spectra recorded at an increasing GdmCl concentration were analyzed by a singular value decomposition algorithm (SVD) using the software MATLAB (Math-Works, South Natick, MA, USA) to remove the high frequency noise and the low frequency random errors, and to determine the number of independent components in any given set of spectra, as described in [ ].



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    Image Search Results


    The top-3 independent components of the spiking response of each trial type. (A) shows each stimulation type and the corresponding independent components over the trial time. Positive coefficients are correlated with spiking activity while negative coefficients are anti-correlated with spiking activity. In the scatter plots below, each component is shown as an axis and each trial is plotted as a point within the three dimensions. Exemplar trials are highlighted and shown in insets with spike rate over time. (B) shows how the component weights (boxes) scale the component shapes to describe the features of the mean firing rate of an example channel. The corresponding blue and green arrows point to the deviations in mean firing rate while the purple arrow and line generally indicate the background firing rate that are captured by the respective component and its weight. (C) shows the reconstruction (shaded yellow) of the mean spike rate of an example channel (black line) using the descriptive weightings of the independent components.

    Journal: Frontiers in Neuroscience

    Article Title: Post-ischemic reorganization of sensory responses in cerebral cortex

    doi: 10.3389/fnins.2023.1151309

    Figure Lengend Snippet: The top-3 independent components of the spiking response of each trial type. (A) shows each stimulation type and the corresponding independent components over the trial time. Positive coefficients are correlated with spiking activity while negative coefficients are anti-correlated with spiking activity. In the scatter plots below, each component is shown as an axis and each trial is plotted as a point within the three dimensions. Exemplar trials are highlighted and shown in insets with spike rate over time. (B) shows how the component weights (boxes) scale the component shapes to describe the features of the mean firing rate of an example channel. The corresponding blue and green arrows point to the deviations in mean firing rate while the purple arrow and line generally indicate the background firing rate that are captured by the respective component and its weight. (C) shows the reconstruction (shaded yellow) of the mean spike rate of an example channel (black line) using the descriptive weightings of the independent components.

    Article Snippet: We first applied principal components analysis (PCA; MATLAB R2017a + ‘pca’ function with ‘Algorithm’ parameter set to ‘svd’) to qualitatively describe the different types of evoked responses for each condition, applying a singular value decomposition to the mean channel spike rates separately for each stimulus type; then, using the groupings for which the same basis subspace could accurately reconstruct the original observations, we seeded a reconstructed-independent components analysis algorithm (r-ICA; MATLAB R2017a + ‘rica’ function from the Statistics and Machine Learning Toolbox) using the top-3 combined-basis eigenvectors to recover a basis for the sets of components described above ( ).

    Techniques: Activity Assay

    Combined independent component analysis of the sensory response and its modulation. (A) shows the mean weights of the components sorted by stimulation type and area which are displayed in (B) . Positive values point to the presence of that component in the response while negative values indicate an inverse relationship; the error bars show the standard error of the mean. (C) displays the prediction of area and lesion volume for component 2 and 3 scores by the GLME model as compared to a linear fit. (D) highlights the changes in the component scores between Solenoid (yellow) and ICMS + Solenoid trials (purple) for each channel in an experimental block of an exemplar animal. (E) shows the reconstructed rates for each stimulation type by area. The mean component scores were used to weight each component and reconstruct the average response in spiking to stimulation.

    Journal: Frontiers in Neuroscience

    Article Title: Post-ischemic reorganization of sensory responses in cerebral cortex

    doi: 10.3389/fnins.2023.1151309

    Figure Lengend Snippet: Combined independent component analysis of the sensory response and its modulation. (A) shows the mean weights of the components sorted by stimulation type and area which are displayed in (B) . Positive values point to the presence of that component in the response while negative values indicate an inverse relationship; the error bars show the standard error of the mean. (C) displays the prediction of area and lesion volume for component 2 and 3 scores by the GLME model as compared to a linear fit. (D) highlights the changes in the component scores between Solenoid (yellow) and ICMS + Solenoid trials (purple) for each channel in an experimental block of an exemplar animal. (E) shows the reconstructed rates for each stimulation type by area. The mean component scores were used to weight each component and reconstruct the average response in spiking to stimulation.

    Article Snippet: We first applied principal components analysis (PCA; MATLAB R2017a + ‘pca’ function with ‘Algorithm’ parameter set to ‘svd’) to qualitatively describe the different types of evoked responses for each condition, applying a singular value decomposition to the mean channel spike rates separately for each stimulus type; then, using the groupings for which the same basis subspace could accurately reconstruct the original observations, we seeded a reconstructed-independent components analysis algorithm (r-ICA; MATLAB R2017a + ‘rica’ function from the Statistics and Machine Learning Toolbox) using the top-3 combined-basis eigenvectors to recover a basis for the sets of components described above ( ).

    Techniques: Blocking Assay