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CodeSignal

Non-linear Dimensionality Reduction Techniques

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Overview

Unravel the complexities of non-linear dimensionality reduction by mastering t-SNE, geared towards unveiling hidden patterns in multifaceted datasets.

Syllabus

  • Unit 1: t-SNE in R
    • Visualizing Iris Dataset with t-SNE
    • Dimensionality Reduction with t-SNE in R
    • Visualizing Iris Dataset with t-SNE
  • Unit 2: tSNE Parameter Tuning in R
    • t-SNE Parameter Tuning on Generated Data
    • Adjusting Perplexity in t-SNE
    • Dimensionality Reduction Engine Parameter Tuning
    • Dimensionality Reduction with t-SNE on Synthetic Circles Dataset
  • Unit 3: Locally Linear Embedding in R
    • Visual Comparison of Dimensionality Reduction Techniques on Swiss Roll Dataset
    • Adjusting LLE Hyperparameters for Swiss Roll Reconstruction
    • Dimensionality Reduction with Locally Linear Embedding
  • Unit 4: Kernel PCA in R
    • Kernel PCA for Non-Linear Image Classification
    • Kernel PCA Parameter Adjustment Task
    • KernelPCA Implementation Task

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