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Direct In-Bone Protein Digestion With Subsequent LC Separation and Trap Ion Mobility MS Detection of Released Peptides as an Effective Tool for the Proteomic Characterization of Bone Tissues. [PDF]
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Comments on the NIPALS algorithm
Journal of Chemometrics, 1990AbstractThe Non‐linear Iterative Partial Least Squares (NIPALS) algorithm is used in principal component analysis to decompose a data matrix into score vectors and eigenvectors (loading vectors) plus a residual matrix. NIPALS starts with some guessed starting vector.
Yoshikatsu Miyashita +3 more
exaly +2 more sources
Determinants of energy consumption in Kenya: A NIPALS approach
Energy, 2018Abstract This study examines the drivers of aggregate energy consumption, fossil fuel and electricity consumption in Kenya using the nonlinear iterative partial least squares (NIPALS) method. The results show the importance of price, population density, urbanization, and renewable energies from hydro sources in promoting energy demand reductions.
Samuel Asumadu-Sarkodie +1 more
exaly +2 more sources
An online NIPALS algorithm for Partial Least Squares
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017Partial Least Squares (PLS) has been gaining popularity as a multivariate data analysis tool due to its ability to cater for noisy, collinear and incomplete data-sets. However, most PLS solutions are designed as block-based algorithms, rendering them unsuitable for environments with streaming data and non-stationary statistics.
Alexander E Stott +2 more
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Hardware Efficient NIPALS Architecture for Principal Component Analysis of Hyper Spectral Images
2019 32nd IEEE International System-on-Chip Conference (SOCC), 2019Principal Component Analysis (PCA) has been a major tool in performing characterization of environmental data where in, the data is typically a hyper spectral image. Using statistical methods, PCA is often capable of reducing the dimensionality of data. On the other hand Nonlinear Iterative PArtial Least Squares (NIPALS) algorithm provides an efficient
Siew-Kei Lam
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2010 Sixth IEEE International Conference on e-Science Workshops, 2010
An implementation of the nonlinear iterative partial least squares algorithm (NIPALS) was used as a test case for use of OpenCL for computation on a general purpose graphics processing unit (GPGPU) cluster using MPI. Timing results are shown along with results of a model of time required per iteration for defined problem sizes.
exaly +2 more sources
An implementation of the nonlinear iterative partial least squares algorithm (NIPALS) was used as a test case for use of OpenCL for computation on a general purpose graphics processing unit (GPGPU) cluster using MPI. Timing results are shown along with results of a model of time required per iteration for defined problem sizes.
exaly +2 more sources