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erichson

rsvd:Randomized Singular Value Decomposition

Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided.

Maintained by N. Benjamin Erichson. Last updated 4 years ago.

dimension-reductionmatrix-approximationpcaprincipal-component-analysisprobabilistic-algorithmsrandomized-algorithmsingular-value-decompositionsvd

60.9 match 98 stars 10.80 score 408 scripts 119 dependents

zilong-li

pcaone:Fast and Accurate Randomized Singular Value Decomposition Algorithms with 'PCAone'

Fast and Accurate Randomized Singular Value Decomposition (RSVD) methods proposed in the 'PCAone' paper by Li (2023) <https://genome.cshlp.org/content/33/9/1599>.

Maintained by Zilong Li. Last updated 19 hours ago.

matrix-factorizationpcarcppeigenrsvdsvdopenblascpp

11.5 match 4 stars 4.00 score 6 scripts

bioc

BiocSingular:Singular Value Decomposition for Bioconductor Packages

Implements exact and approximate methods for singular value decomposition and principal components analysis, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Where possible, parallelization is achieved using the BiocParallel framework.

Maintained by Aaron Lun. Last updated 5 months ago.

softwaredimensionreductionprincipalcomponentbioconductor-packagehuman-cell-atlassingular-value-decompositioncpp

2.0 match 7 stars 12.10 score 1.2k scripts 99 dependents

r-forge

wordspace:Distributional Semantic Models in R

An interactive laboratory for research on distributional semantic models ('DSM', see <https://en.wikipedia.org/wiki/Distributional_semantics> for more information).

Maintained by Stephanie Evert. Last updated 3 months ago.

cppopenmp

3.3 match 4.95 score 150 scripts 2 dependents

thomaschln

nlpembeds:Natural Language Processing Embeddings

Provides efficient methods to compute co-occurrence matrices, pointwise mutual information (PMI) and singular value decomposition (SVD). In the biomedical and clinical settings, one challenge is the huge size of databases, e.g. when analyzing data of millions of patients over tens of years. To address this, this package provides functions to efficiently compute monthly co-occurrence matrices, which is the computational bottleneck of the analysis, by using the 'RcppAlgos' package and sparse matrices. Furthermore, the functions can be called on 'SQL' databases, enabling the computation of co-occurrence matrices of tens of gigabytes of data, representing millions of patients over tens of years. Partly based on Hong C. (2021) <doi:10.1038/s41746-021-00519-z>.

Maintained by Thomas Charlon. Last updated 25 days ago.

1.8 match 4.98 score

yixinww

ltsspca:Sparse Principal Component Based on Least Trimmed Squares

Implementation of robust and sparse PCA algorithm of Wang and Van Aelst (2019) <DOI:10.1080/00401706.2019.1671234>.

Maintained by Yixin Wang. Last updated 5 years ago.

openblascpp

1.6 match 3.70 score 4 scripts

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