Questions tagged [tsne]

T-distributed stochastic neighbor embedding (t-SNE) is a nonlinear dimensionality reduction algorithm introduced by van der Maaten and Hinton in 2008.

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Any reason for choosing t-SNE over UMAP when visualizing?

According to the UMAP paper: Our algorithm is competitive with t-SNE for visualization quality and arguably preserves more of the global structure with superior run time performance. paper It seems ...
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tSNE on OCTMNIST dataset (part of MedMNIST) not working

I have been trying to generate the tSNE plot for the OCTMNIST dataset which is a part of MedMNIST v2(https://medmnist.com/). The code that I have been using is- ...
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What is wrong with using t-SNE for predictions?

If I have a dataset with hundreds of samples and thousands of features, and t-SNE does a good job of separating classes compared to others classifiers, I don't understand why I can't rerun the ...
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sklearn vs RtSNE : Number of PCA components to retain in tSNE [closed]

In the R implementation you can pass the number of components to keep in PCA step. I cannot figure out if this is possible in sklearn implementation. Is it possible or I am missing it? Thank you! R: ...
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How do interpret this result for t-SNE plot results?

I am trying to interpret this plot I created for a malware dataset. The dataset contains Benign and Malware data. I am having a hard time understanding what it means. Any advice or help would be ...
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Variance used in t-SNE

I was going through the t-SNE, but I am bit confused. While the original paper of t-SNE is based on the SNE and SNE uses $\sigma_i^2$ (note the subscript $i$) while calculating the similarity of point ...
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How to reveal specific relationship using tSNE?

I’ve been using tSNE in attempts of finding and visualising some relationships in my data. I came to a following problem. If my data is say 1000 points, and I want to know about some relations between ...
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Should neural net features be scaled before applying t-SNE?

It is considered good practice to scale the data (zero mean and unit variance) before using an algorithm like t-SNE or UMAP, such that all features are given the same importance If the features are ...
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Performance differences between variational autoencoder and t-SNE

I trained a convolutional variational autoencoder on a dataset of medical images to detect anomalies. Based on the reconstruction error, I try to distinguish between normal images and anomalies. In ...
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t-SNE number of output components

Why do most tSNE implementations suggest using 2 or 3 output dimensions? For PCA, the number of output components is typically choosen based on the number of components needed to explain 80% of ...
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Are t-SNE and UMAP for dimensionality reduction compatible with cross-validation in machine learning applications?

t-SNE: Based on how I understand the original t-SNE algorithm, it requires a whole dataset for doing the transformation. That is, there are no distinct "fitting" and "transformation&...
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How to interpret axis of UMAP?

Hi in PCA one can interpret the level of variance with x vs y, with x often times explaining much of the variance. As one plot higher dimensions the variance percent would go down. My question is, ...
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How does t-SNE preserves embedding orders?

According to the triplet loss Wikipedia page: t-SNE (t-distributed Stochastic Neighbor Embedding) preserves embedding orders via probability distributions, whereas triplet loss works directly on ...
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t-SNE perplexity for groups of different sizes

I have a data set of about 1200 samples that I expect to be divided in to about 40 groups but the group sizes vary from about 5 to 150 samples. About half of the groups have 10-40 samples, 10 have ...
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What happens if I increased the number of principal components used in t-SNE?

I see that most of the tutorials in t-SNE use mostly the top 10 PCs to run t-SNE, I tried to run top 100 PCs and top 500 PCs, the results are different but I don't really get the changes Thank you
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Can I use t-SNE for checking the goodness of a clustering based on Gower distance matrix?

I am currently working in Python with a dataset with both categorical and continuous variables. The main objective is to do clustering and find how different features help to create the clusters. This ...
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What dimensionality reduction methods allow a lower dimensional reconstruction of the original data besides PCA via invertible transformations?

In eigenfaces, one used the inverse transformation PCA is capable of doing to reconstruct the low dimensional face image. In tsne one may not reconstruct the original dataset to produce something akin ...
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Data nicely separated by UMAP but less by T-SNE

If some data are nicely separated into clusters using UMAP but not as nicely using T-SNE, what could be the interpretation ?
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How to interpret data not separated by PCA but by T-sne/UMAP

I have a classification problem, to have a first look at my data I do a PCA followed by TSNE and UMAP. My clusters are nicely separated by TSNE and UMAP but not by PCA. Does it have implication for ...
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Interpret t-SNE plot groups

I am trying to interpret some t-SNE results (using the Rtsne package in R). I used t-SNE on class probabilities from a multiclass model with 6 levels to try and visualize the relationships between the ...
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t-SNE and normalization / standardization

I have 7 parameters with different scales. If I understand correctly, before applying t-SNE or other dimension reduction methods I should apply a standardization or normalization method on my ...
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Randomness of t-SNE

The stochastic part of t-SNE is presumably from the randomness of the initial placement of the points in the low-dimensional space. Does this mean that t-SNE should be re-run on re-seeded randomness ...
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Circles in tSNE output

Does anybody know when tSNE produces circles as embeddings (as a property of the input features)? I've ran tSNE with the default params in R Rtsne function.
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Linear and nonlinear feature extraction methods

I far what i understand, the assumptions for PCA is that data should be linear. What does that imply? let say if i have a data of 1000*100. So all independent variables should be linearly separable ...
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Multivariate Jensen-Shannon divergence

This paper says multivariate Jensen-Shannon divergence is $$JS(\mathbf{p}_1,\dots,\mathbf{p}_K) = \frac{1}{m} \sum KL(\mathbf{p}_i || \bar{\mathbf{p}})$$ with $KL$ being the KL-divergence of the ...
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Why doesn't t-SNE need the labels before visualization?

In the original Maaten and Hinton paper, they explicitly say that the class membership is not used by the t-SNE calculations, only for picking colors in the plot. For all of the data sets, there is ...
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multiple tsne (and pca) transforms on one plot

I'm reviewing a paper, in which the researcher fitted different t-sne transforms and plotted them on one plot, showing clusters in 2-d after dimensionality reduction (t-sne). Each cluster was fitted ...
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Can t-SNE be directly used as a clustering algorithm?

I've been working on a dataset about a few millions commuters and their travel patterns with around 50 dimensions. And I'm applying t-SNE to the dataset. My initial goal of applying t-SNE was to ...
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t-sne embedding to medium-dimensions (e.g. 100 dimensions)?

I am using t-sne on 252 dimensional data to embed to lower-dimensions. I am curious to know if it is academically justifiable to embed it into medium dimensions such as 100 dimensions, or 80 ...
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Understand important features in UMAP

I am using a dimensionality reduction algorithm (UMAP) to cluster high-dimensional data. Particularly, I have ~50000 vectors of dimension ~20000 to visualise. These vectors are highly structured: ...
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PCA provides principal directions, what does tSNE provide?

One of my main frustrations with the current state of single cell transcriptome analysis is representations of cells within $tSNE$ plots. These $tSNE$ plots provide amazing separation of the data and ...
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What is the connection between fat tailedness of student t distribution and sparsity inducement in lower dimensions in context of t-sne

I have read that the t distribution is a heavy tailed distribution in comparison to Normal distribution. Also some say that heavy tailed distributions help in creating more sparsity. My question is ...
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Connection between Stochastic Neighbor Embedding and MDS

In the original SNE paper the authors mention a connection between the SNE objective function and an MDS-like stress function in the regime $\sigma_i \rightarrow \infty$, as follows. When $\sigma_i^...
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Can the sum of two conditional probability distributions give a joint probability distribution?

A paper describing symmetric SNE for the conditional probability distribution $$p_{j|i}=\frac{e^{-\left|x_i-x_j\right|^2/2\sigma_i}}{\displaystyle\sum_{k\neq i}e^{-\left|x_k-x_i\right|^2/2\sigma_i}}$$...
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SVD -> PCA -> t-SNE; Does it make sense?

I have a data set of size (4600, 10000). I did L2 normalization at first, then I did the following two steps to visualize it in a lower dimension: ...
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Perform normalization before using t-SNE components in any ML algorithms and while appending new features to these components?

I have a large data set (around 4600 rows and 10000 columns). First, I performed L2 normalization and did PCA to obtained 50 components. Then I performed t-SNE and obtained 2 components. I did the ...
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Interpreting snake-like structures in the UMAP visualization of a FASTA data set

I'm looking for some guidance to interpret a UMAP plot. I started with two FASTA files for two different genes. I concatenated everything into a single string of only ACGT. Then I split that ...
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29 votes
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Intuitive explanation of how UMAP works, compared to t-SNE

I have a PhD in molecular biology. My studies recently started to involve high dimensional data analysis. I got the idea of how t-SNE works (thanks to a StatQuest video on YouTube) but can't seem to ...
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t-SNE with mixed continuous and binary variables

I am currently investigating the visualisation of high-dimensional data using t-SNE. I have some data with mixed binary and continuous variables and the data appears to cluster the binary data much ...
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Why does larger perplexity tend to produce clearer clusters in t-SNE?

Why does larger perplexity tend to produce clearer clusters in t-SNE? By reading the original paper, I learned that the perplexity in t-SNE is $2$ to the power of Shannon entropy of the conditional ...
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7 votes
1 answer
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How can t-SNE or UMAP embed new (test) data, given that they are nonparametric?

I have started using the UMAP method for dimension reduction which is a similar method to t-SNE, Diffusion Maps, Laplacian Eigenmaps, etc. The named dimension reduction methods have in common that ...
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Perplexity formula in the t-SNE paper vs. in the implementation

The perplexity formula in the official paper of t-SNE IS NOT the same as in its implementation. In the implementation (MATLAB): ...
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How do I intepret these t-SNE results?

I am rather new to the TSNE method, and am learning about the various pitfalls associated with interpreting it correctly. I have performed TSNE on a very-high dimensional dataset (>20,000 dimensions) ...
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How is Student's t-distribution related with this similarity/probability equation between data points?

In the t-SNE paper "Visualizing Data using t-SNE" and a Deep Embedded Clustering (DEC) approach "Unsupervised Deep Embedding for Clustering Analysis", they both use the Student t-distribution to ...
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Why does modelling t-SNE 2D coordinates with a neural network fail to produce a perfect fit?

In short, I want to train a neural network to project my high dimensional objects to a 2D plane coordinates which was created using t-SNE (Rtsne package for R). I ...
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Why can't t-SNE capture a simple parabola structure?

As a toy example, I used t-SNE on a simple parabola to have a representation of it in one dimension. ...
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Does t-SNE in 3 dimensions offer any additional information to 2 dimensional t-SNE?

t-SNE is a data reduction technique that primarily gives information about local similarities. Clustering in a t-SNE graph usually (but not always) indicates some hidden structure in the higher-...
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Is there a way to reduce high-dimensional feature space to an array of 2d tSNEs ordered along a chosen dimension?

Let's say we have 4096-d vectors (via a CNN fully-connected layer) and often we use tSNE to visualize the space, sometimes in combo with Jonker-Volgenant to assign it to a grid. When applied to image ...
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Can t-SNE help feature selection?

I'm training a fully connected feed forward neural network for regression. Given one training example $(x_i, y_i)$, I need to convert the raw representation $x_i$ into an invariant representation $...
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Using UMAP or other non-linear dimension reduction techniques on response variables prior to learning?

Background Suppose you have a training set where the response measurements are some $N$-dimensional vectors of related measurements - in my specific case, they happen to be cell viability scores for ...
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