Similarity Weighted Nonnegative Embedding (SWNE), is a method for visualizing high dimensional datasets. SWNE uses Nonnegative Matrix Factorization to decompose datasets into latent factors, projects those factors onto 2 dimensions, and embeds samples and key features in 2 dimensions relative to the factors. SWNE can capture both the local and global dataset structure, and allows relevant features to be embedded directly onto the visualization, facilitating interpretation of the data.
Package details |
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Author | Yan Wu |
Maintainer | Yan Wu <yauwning@gmail.com> |
License | GPL-2 |
Version | 0.6.20 |
Package repository | View on GitHub |
Installation |
Install the latest version of this package by entering the following in R:
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