# Questions tagged [tsne]

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

14 questions with no upvoted or accepted answers
Filter by
Sorted by
Tagged with
1answer
81 views

### 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 ...
1answer
84 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 ...
2answers
194 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 ...
0answers
150 views

### 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 ...
0answers
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 ...
0answers
14 views

### Why is a 2D->2D tSNE transformation not idempotent (up to random influences)?

My understanding of tSNE is that probability distributions are optimized in a way to maintain Euclidean (by default) distances (on average) when transforming from the input to the output space. If ...
0answers
60 views

### 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 ...
2answers
92 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
0answers
127 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 ...
0answers
55 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 ...
1answer
57 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. ...
0answers
103 views

### (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 ...
0answers
19 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 ...
1answer
291 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 ...