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using the same color names, comparing different dimensionality reduction algorithms (pca, tsne and umap):

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Kat ⊷ the Poet Engineer

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31,348 次观看 • 1 年前 •via X (Twitter)

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Oldies but goldies: M. Belkin, P. Niyogi, Laplacian Eigenmaps for Dimensionality Reduction and Data Representation, 2003. Non-linear dimensionality reduction by embedding data points using the eigenvectors of a graph Laplacian as coordinates.
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Oldies but goldies: M. Belkin, P. Niyogi, Laplacian Eigenmaps for Dimensionality Reduction and Data Representation, 2003. Non-linear dimensionality reduction by embedding data points using the eigenvectors of a graph Laplacian as coordinates.

Gabriel Peyré

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Many dimensionality reduction algorithms share a few central principles. 1. Construct a graph that captures the data's local structure 2. Measure "geodesic" distances between points using the graph 3. Project the points to a lower dimension while preserving these distances
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Many dimensionality reduction algorithms share a few central principles. 1. Construct a graph that captures the data's local structure 2. Measure "geodesic" distances between points using the graph 3. Project the points to a lower dimension while preserving these distances

Alec Helbling

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Laplacian eigenmaps perform non-linear dimensionality reduction ("manifold learning") by embedding data points using the eigenvectors of a graph Laplacian as coordinates.
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Laplacian eigenmaps perform non-linear dimensionality reduction ("manifold learning") by embedding data points using the eigenvectors of a graph Laplacian as coordinates.

Gabriel Peyré

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They call the exact same bird two different names in the same film, that's actually really funny 😭
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the flashlight method of comparing two projections of the same embeddings 🔦 2M points in webgl, color by spread/compaction, umaps aligned by procrustes.
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the flashlight method of comparing two projections of the same embeddings 🔦 2M points in webgl, color by spread/compaction, umaps aligned by procrustes.

Ian Johnson 🔬🤖

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I wrote this fun interactive article explaining Isomap, a classic non-linear dimensionality reduction technique. Isomap captures the local similarity structure of data through a graph and computes an embedding that preserves this structure. Check it out! Link below.
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I wrote this fun interactive article explaining Isomap, a classic non-linear dimensionality reduction technique. Isomap captures the local similarity structure of data through a graph and computes an embedding that preserves this structure. Check it out! Link below.

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a friend brought up Danganronpa and that reminded me of one of the funniest examples of two different products using the same sample
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a friend brought up Danganronpa and that reminded me of one of the funniest examples of two different products using the same sample

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Check out the same lift using different Weights. Top is 124lb Fatbells Bottom is 125lb DB’s
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New feature✨ You can now set the color profile when creating a new canvas🎨 See the same colors when working across different devices.
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🐶 huh… but comparing people to others is seriously the worst. there are billions of people in the world and every single one of them is different, yet you still try to measure everyone by the same standard. seriously… that’s just too much and pathetic
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🐶 huh… but comparing people to others is seriously the worst. there are billions of people in the world and every single one of them is different, yet you still try to measure everyone by the same standard. seriously… that’s just too much and pathetic

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