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02_​PCA_​t-SNE

Dimensionality Reduction with PCA and t-SNE

This workflow applyes two dimensionality reduction techniques:
-PCA
-t-SNE
to reduce the dataset dimensions from three to two features.
Data points are then displayed in a scatter plot.

Here PCA and t-SNE techniques reduce data dimensions to just two features t-SNE 2 dimensionsPCA - 2 dimensionsPlot the extractedprincipal componentsPlot the extractedprincipal componentsRead outlierdataset t-SNE (L. Jonsson) PCA Scatter Plot Scatter Plot File Reader Here PCA and t-SNE techniques reduce data dimensions to just two features t-SNE 2 dimensionsPCA - 2 dimensionsPlot the extractedprincipal componentsPlot the extractedprincipal componentsRead outlierdatasett-SNE (L. Jonsson) PCA Scatter Plot Scatter Plot File Reader

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: rosy
Created at: 2014-10-01
On NodePit since: 2024-03-10
Last update: 2026-03-21
Created with KNIME version: v4.7.0
Tags: TheGuideBookscatter plotPCAPrincipal Component Analysist-SNEdimensionality reductionGIDS

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