Questions tagged [tsne]

t-SNE (t-distributed stochastic neighbor embedding) is a technique for dimensionality reduction.

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What Clustering Method Should I Use?

My data is a group of 10 thousand points (each having an node location (x,y)) that are spread across a plane. They are also chromatically-colored based on their weight. I need to finalize a bayesian ...
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1answer
73 views

How to reduce position changes after dimensionality reduction?

Disclaimer: I'm a machine learning beginner. I'm working on visualizing high dimensional data (text as tdidf vectors) into the 2D-space. My goal is to label/modify those data points and recomputing ...
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2answers
170 views

Grouping already clustered data (with a pre-defined x and y)

I have an already clustered data set (I wanna keep my x and y), where there's clearly a small group of elements in the middle that don't follow the expected patterns. I can select them manually, but ...
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t-SNE on extremely high-dimensional spaces

I successfully applied t-SNE to the number handwriting dataset. n=3823 data points (i.e. handwritten numbers) in an D=64 dimensional space (i.e. 8x8 pixels). Worked great. Now I would like to cluster ...
2
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1k views

t-SNE plotting DBSCAN clustering results very scattered issue

We are trying a DBSCAN clustering model on our 30,000 samples with 15 features each. We tuned the epsilon parameter small enough to make sure the radius of the clustering circle is small while it does ...
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How to plot centroids and clusters resulting from a KMean model based on a text variable

I hope you can help as after several attempts, I'm no longer sure I can get a decent result. I have a text corpus made of several documents, like the one below (which has been simplified for the sake ...
1
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0answers
80 views

Approximating t-SNE embeddings for out-of-sample data

I have a large amount of data which has been reduced to two dimensions using t-SNE. Additional data points keep arriving, which I would like two-dimensional embeddings for, but this cannot be achieved ...
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46 views

Is there a representation of the separating hyperplane in t-sne?

I have used t-sne to visualize a set of images which I have used for training a binary classifier. Let us assume that the binary classifier is trained to detect cat(1) vs. no-cat(0). I have used the ...
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123 views

tsne plot not showing all the labels?

My data has 13 labels in total. But in tsne plot, the figure just displays 10 labels with the other 2 labels as "Other" in the same color. How can I set the plot to show all of the labels? Thanks.
0
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1answer
29 views

what information can we obtain from t-SNE?

I see that t-SNE can help us reduce dimensions and visualize the data. But what information are we gaining from this visualization? As we know that the new axis don't have a meaning in our context. ...
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(UMAP) Why does umap.transform() put new data in the periphery of old clusters?

UMAP is a dimensionality reduction method like t-SNE and PCA. Unlike t-SNE, UMAP can incorporate new data and do a forward transform on the data. While experimenting, I made an interesting observation ...
0
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2answers
31 views

If in t-SNE digaram of binary classification both classes follow the similar curve what does t-SNE diagram show?

If in t-SNE digaram of binary classification both classes follow the similar curve what does t-SNE diagram show for instance: Figure1 or Figure2
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16 views

Suggestions on non-linear dimensionality reduction for small, one-hot encoded dataset

I wish to apply non-linear dimensionality reduction on a very small dataset (less than 100 observations). The dataset is very sparse, of approx 20 columns, each containing either 0 or 1. It's the ...
0
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1answer
253 views

How do I calculate a similarity matrix with a Student-t kernel?

As the title says, how do I calculate a similarity matrix with an un-normalized Student-t kernel? I'm attempting to calculate Kullback-Leibler divergence for different t-SNE runs, but need a Q-matrix ...