scientific-pca-tsne

Community

Visualize high-D data with PCA, t-SNE, and UMAP.

Authornahisaho
Version1.0.0
Installs0

System Documentation

What problem does it solve?

High-dimensional data visualization and exploration are challenging; this skill provides dimensionality-reduction methods to map data into 2D/3D spaces for clearer structure interpretation and pattern discovery.

Core Features & Use Cases

  • PCA for linear dimensionality reduction and interpretable variance contributions.
  • t-SNE and UMAP for nonlinear embeddings that preserve local/global structure.
  • Use cases include visualizing chemical space, material feature landscapes, and multi-technique data fusion to identify clusters and trends.

Quick Start

Apply PCA, t-SNE, and UMAP to your dataset to generate 2D/3D embeddings and visualize the results.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

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Please help me install this Skill:
Name: scientific-pca-tsne
Download link: https://github.com/nahisaho/satori/archive/main.zip#scientific-pca-tsne

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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