Spca
Scalable PCA (sPCA) is a scalable implementation of Principal component analysis algorithm on top of Spark
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| H2o 3 | 6,618 | 62 | 33 | 4 months ago | 49 | August 09, 2023 | 2,746 | apache-2.0 | Jupyter Notebook | |
| H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc. | ||||||||||
| Spark Examples | 75 | 7 years ago | 10 | apache-2.0 | Scala | |||||
| Apache Spark jobs such as Principal Coordinate Analysis. | ||||||||||
| Machinelearning | 14 | 6 years ago | Python | |||||||
| Spark_mllib_algorithm_1.6.0 | 11 | 7 years ago | Scala | |||||||
| Spark Mllib 1.6.0版本算法封装 | ||||||||||
| Matrixproductpca | 8 | 7 years ago | apache-2.0 | Scala | ||||||
| Code for our paper "Single Pass PCA of Matrix Products" | ||||||||||
| Sparkandmpifactorizations | 7 | 8 years ago | mit | Scala | ||||||
| implementations of CX, PCA, and NMF factorizations in Spark and MPI | ||||||||||
| Spark_projects | 6 | 9 years ago | Python | |||||||
| Spark Projects for the Berkeley Data Science Course | ||||||||||
| Spca | 5 | 9 years ago | mit | Java | ||||||
| Scalable PCA (sPCA) is a scalable implementation of Principal component analysis algorithm on top of Spark | ||||||||||
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