regularized least-squares regression using the lasso algorithm ( l 1 norm) Search Results


csu-w1  (Yokogawa Electric)
99
Yokogawa Electric csu-w1
Csu W1, supplied by Yokogawa Electric, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/csu-w1/product/Yokogawa Electric
Average 99 stars, based on 1 article reviews
csu-w1 - by Bioz Stars, 2026-06
99/100 stars
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infomax ica  (InfoMax Inc)
90
InfoMax Inc infomax ica
Classification accuracy averaged across 304 traditionally preprocessed data scans in predicting whether a subject was viewing a video, listening to an audio stimuli, or resting, using 20 components. Chance accuracy is 50%.
Infomax Ica, supplied by InfoMax 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/result/infomax ica/product/InfoMax Inc
Average 90 stars, based on 1 article reviews
infomax ica - by Bioz Stars, 2026-06
90/100 stars
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nacl  (Sinopharm ltd)
90
Sinopharm ltd nacl
Classification accuracy averaged across 304 traditionally preprocessed data scans in predicting whether a subject was viewing a video, listening to an audio stimuli, or resting, using 20 components. Chance accuracy is 50%.
Nacl, supplied by Sinopharm ltd, 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/result/nacl/product/Sinopharm ltd
Average 90 stars, based on 1 article reviews
nacl - by Bioz Stars, 2026-06
90/100 stars
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sodium hypochlorite (naclo)  (Sinopharm ltd)
90
Sinopharm ltd sodium hypochlorite (naclo)
Classification accuracy averaged across 304 traditionally preprocessed data scans in predicting whether a subject was viewing a video, listening to an audio stimuli, or resting, using 20 components. Chance accuracy is 50%.
Sodium Hypochlorite (Naclo), supplied by Sinopharm ltd, 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/result/sodium hypochlorite (naclo)/product/Sinopharm ltd
Average 90 stars, based on 1 article reviews
sodium hypochlorite (naclo) - by Bioz Stars, 2026-06
90/100 stars
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murashige & skoog (ms) basal salt mixture  (PhytoTechnology Laboratories)
90
PhytoTechnology Laboratories murashige & skoog (ms) basal salt mixture
Classification accuracy averaged across 304 traditionally preprocessed data scans in predicting whether a subject was viewing a video, listening to an audio stimuli, or resting, using 20 components. Chance accuracy is 50%.
Murashige & Skoog (Ms) Basal Salt Mixture, supplied by PhytoTechnology Laboratories, 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/result/murashige & skoog (ms) basal salt mixture/product/PhytoTechnology Laboratories
Average 90 stars, based on 1 article reviews
murashige & skoog (ms) basal salt mixture - by Bioz Stars, 2026-06
90/100 stars
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Image Search Results


Classification accuracy averaged across 304 traditionally preprocessed data scans in predicting whether a subject was viewing a video, listening to an audio stimuli, or resting, using 20 components. Chance accuracy is 50%.

Journal: Journal of neuroscience methods

Article Title: 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

doi: 10.1016/j.jneumeth.2017.03.008

Figure Lengend Snippet: Classification accuracy averaged across 304 traditionally preprocessed data scans in predicting whether a subject was viewing a video, listening to an audio stimuli, or resting, using 20 components. Chance accuracy is 50%.

Article Snippet: We evaluate not only the general algorithms, but also their varied implementations, including four variations of ICA (Entropy Bound Minimization [EBM ICA], Fast ICA, InfoMax ICA, Joint Approximate Diagonalization of Eigen-matrices [JADE ICA]), two variations of NMF (Alternating Least Squares [NMF-ALS], Projected Gradient [NMF-PG]), and two sparse coding algorithms ( L 1 Regularized Learning, K-SVD).

Techniques:

Classification accuracy averaged across 304 traditionally preprocessed scans in predicting whether a subject was viewing a video, listening to an audio stimuli, or resting, using 50 components. Chance accuracy is 50%.

Journal: Journal of neuroscience methods

Article Title: 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

doi: 10.1016/j.jneumeth.2017.03.008

Figure Lengend Snippet: Classification accuracy averaged across 304 traditionally preprocessed scans in predicting whether a subject was viewing a video, listening to an audio stimuli, or resting, using 50 components. Chance accuracy is 50%.

Article Snippet: We evaluate not only the general algorithms, but also their varied implementations, including four variations of ICA (Entropy Bound Minimization [EBM ICA], Fast ICA, InfoMax ICA, Joint Approximate Diagonalization of Eigen-matrices [JADE ICA]), two variations of NMF (Alternating Least Squares [NMF-ALS], Projected Gradient [NMF-PG]), and two sparse coding algorithms ( L 1 Regularized Learning, K-SVD).

Techniques:

Classification accuracy averaged across 304 artifact-cleaned scans in predicting whether a subject was viewing a video, listening to an audio stimuli, or resting, using 20 components. Chance accuracy is 50%.

Journal: Journal of neuroscience methods

Article Title: 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

doi: 10.1016/j.jneumeth.2017.03.008

Figure Lengend Snippet: Classification accuracy averaged across 304 artifact-cleaned scans in predicting whether a subject was viewing a video, listening to an audio stimuli, or resting, using 20 components. Chance accuracy is 50%.

Article Snippet: We evaluate not only the general algorithms, but also their varied implementations, including four variations of ICA (Entropy Bound Minimization [EBM ICA], Fast ICA, InfoMax ICA, Joint Approximate Diagonalization of Eigen-matrices [JADE ICA]), two variations of NMF (Alternating Least Squares [NMF-ALS], Projected Gradient [NMF-PG]), and two sparse coding algorithms ( L 1 Regularized Learning, K-SVD).

Techniques:

Classification accuracy of BSS algorithm compared to Fast ICA, using 20 components extracted from artifact-cleaned scans, in order of performance from worst to best. Time series weights from InfoMax ICA, JADE ICA, NMF-PG, NMF-ALS predicted activity significantly worse than Fast ICA, while L1-Regularization did significantly better ( p < 0.001). Baseline is set to Fast ICA untuned SVM classification accuracy. Scan-ID and Subject-ID were included as random effects within a general linear mixed-effects regression model to adjust for multiple comparisons.

Journal: Journal of neuroscience methods

Article Title: 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

doi: 10.1016/j.jneumeth.2017.03.008

Figure Lengend Snippet: Classification accuracy of BSS algorithm compared to Fast ICA, using 20 components extracted from artifact-cleaned scans, in order of performance from worst to best. Time series weights from InfoMax ICA, JADE ICA, NMF-PG, NMF-ALS predicted activity significantly worse than Fast ICA, while L1-Regularization did significantly better ( p < 0.001). Baseline is set to Fast ICA untuned SVM classification accuracy. Scan-ID and Subject-ID were included as random effects within a general linear mixed-effects regression model to adjust for multiple comparisons.

Article Snippet: We evaluate not only the general algorithms, but also their varied implementations, including four variations of ICA (Entropy Bound Minimization [EBM ICA], Fast ICA, InfoMax ICA, Joint Approximate Diagonalization of Eigen-matrices [JADE ICA]), two variations of NMF (Alternating Least Squares [NMF-ALS], Projected Gradient [NMF-PG]), and two sparse coding algorithms ( L 1 Regularized Learning, K-SVD).

Techniques: Activity Assay

Greater sparsity for an extracted spatial map was associated with a higher classification accuracy in predicting a subject’s task during scan time when using those spatial maps for encoding ( p < 0.001), holding constant the effect of the algorithm. Using 20 components extracted from artifact-cleaned scans, sparsity was measured using the negative averaged number of zero-valued voxels of all spatial maps, which is insensitive to the scaling of the individual algorithms. Baseline is set to Fast ICA untuned SVM classification accuracy. Scan-ID and Subject-ID were included as random effects within a general linear mixed-effects regression model to adjust for multiple comparisons.

Journal: Journal of neuroscience methods

Article Title: 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

doi: 10.1016/j.jneumeth.2017.03.008

Figure Lengend Snippet: Greater sparsity for an extracted spatial map was associated with a higher classification accuracy in predicting a subject’s task during scan time when using those spatial maps for encoding ( p < 0.001), holding constant the effect of the algorithm. Using 20 components extracted from artifact-cleaned scans, sparsity was measured using the negative averaged number of zero-valued voxels of all spatial maps, which is insensitive to the scaling of the individual algorithms. Baseline is set to Fast ICA untuned SVM classification accuracy. Scan-ID and Subject-ID were included as random effects within a general linear mixed-effects regression model to adjust for multiple comparisons.

Article Snippet: We evaluate not only the general algorithms, but also their varied implementations, including four variations of ICA (Entropy Bound Minimization [EBM ICA], Fast ICA, InfoMax ICA, Joint Approximate Diagonalization of Eigen-matrices [JADE ICA]), two variations of NMF (Alternating Least Squares [NMF-ALS], Projected Gradient [NMF-PG]), and two sparse coding algorithms ( L 1 Regularized Learning, K-SVD).

Techniques:

Encodings using spatial maps with high intensity in CSF regions had reduced classification accuracy, while spatial maps with variable extractions in white-matter and grey-matter regions had higher classification accuracy. Baseline is set to Fast ICA untuned SVM classification accuracy. Scan-ID and Subject-ID were included as random effects within a general linear mixed-effects regression model to adjust for multiple comparisons.

Journal: Journal of neuroscience methods

Article Title: 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

doi: 10.1016/j.jneumeth.2017.03.008

Figure Lengend Snippet: Encodings using spatial maps with high intensity in CSF regions had reduced classification accuracy, while spatial maps with variable extractions in white-matter and grey-matter regions had higher classification accuracy. Baseline is set to Fast ICA untuned SVM classification accuracy. Scan-ID and Subject-ID were included as random effects within a general linear mixed-effects regression model to adjust for multiple comparisons.

Article Snippet: We evaluate not only the general algorithms, but also their varied implementations, including four variations of ICA (Entropy Bound Minimization [EBM ICA], Fast ICA, InfoMax ICA, Joint Approximate Diagonalization of Eigen-matrices [JADE ICA]), two variations of NMF (Alternating Least Squares [NMF-ALS], Projected Gradient [NMF-PG]), and two sparse coding algorithms ( L 1 Regularized Learning, K-SVD).

Techniques: