T-Distributed Stochastic Neighbor Embedding (t-SNE)

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• Principal Component Analysis (PCA)
• Embedding
  • Local Linear Embedding (LLE)
• Dimensional Reduction Algorithms
• Softmax
• Pooling / Sub-sampling: Max, Mean
• (Deep) Convolutional Neural Network (DCNN/CNN)
• Seven Techniques for Dimensionality Reduction | KNIME
• Principal Component Analysis (PCA) ...linear
• Embedding Projector

T-distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm for visualization developed by Laurens van der Maaten and Geoffrey Hinton. It is a nonlinear dimensionality reduction technique well-suited for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions. Specifically, it models each high-dimensional object by a two- or three-dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points with high probability. Wikipedia