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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19 views

Conditional and joint probabilities in tSNE

I have been trying to figure out why $$p_{i|j} = \frac{exp \left(-\left\|x_i - x_j\right\|^{2} / 2\sigma_i ^{2}\right)}{\sum_{k \neq i} exp \left(-\left\|x_i - x_k\right\|^{2} / 2\sigma_i ^{2}\right)}$...
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42 views

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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29 views

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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26 views

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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24 views

t-SNE plot for regression model?

I am trying to plot a hidden layer output using t-SNE but my problem is a regression task. I convert label into five equal intervals (0-5) to demonstrate purpose. I plot this t-SNE in the test set. ...
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66 views

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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30 views

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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22 views

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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17 views

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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17 views

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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76 views

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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28 views

t-SNE can destroy separability, but can it create it?

The t-SNE algorithm is helpful for visualizing a low-dimension representation of data in dimensions too high for us to imagine. In compressing the data from $\mathbb{R}^{100}$ to $\mathbb{R}^{2}$, ...
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34 views

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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20 views

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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435 views

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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42 views

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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451 views

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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60 views

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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50 views

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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36 views

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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53 views

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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309 views

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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578 views

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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375 views

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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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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2k views

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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1k views

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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1answer
128 views

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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761 views

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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1answer
577 views

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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238 views

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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1answer
164 views

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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499 views

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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72 views

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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1answer
623 views

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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521 views

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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471 views

Log-transformation of Compositional Data

I am dealing with compositional data, in a high dimension. Each sample I have behaves like: $$ {S}^D=\left\{\mathbf{x}=[x_1,x_2,\dots,x_D]\in\mathbb{R}^D \,\left|\, x_i>0,i=1,2,\dots,D; \sum_{i=1}...
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358 views

Where is t-distribution used in t-SNE?

I am trying to learn the dimensionality reduction using t-SNE technique. After some videos and explanation I understood the idea behind it. But I am not getting where the t-distribution is used behind ...
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62 views

Interpreting t-SNE dimensions in terms of the original features [duplicate]

I have a dataset of 223 multilabels. I'm sure some of those labels are correlated so I want to merge them. For that I'm using sklearn tSNE to reduce the dimensions. How can I "describe" the new ...
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538 views

Intuition behind perplexity parameter in t-SNE

While reading Laurens van der Maaten's paper about t-SNE we can encounter the following statement about perplexity: The perplexity can be interpreted as a smooth measure of the effective number of ...
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176 views

Dimensionality reduction: include labels?

Lets say I have something like the iris dataset, the columns are petal length, petal width, ...
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1answer
289 views

Dimensionality reduction with least distance distortion

Question: Could I find a dimensionality reduction algorithm without or with minimal distance (cosine) distortion? Background: I would like to visualize in 2D a sample of news texts for which I also ...
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954 views

Differences between t-SNE and SOM

I have some high dimensional data and I want to reduce it to 2 dimensions for visualization. The goal is to color the points in this 2D space to see whether there is any clustering due to different ...
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700 views

Can we apply KL divergence to the probability distributions on different domains?

When I was reading the original paper of t-SNE, I had an question whether or not we can apply KL divergence to the discrete probability distributions on different domains. In the paper, they measure ...
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1answer
794 views

t-SNE on principal component scores: standardization needed?

I have a huge dataset (1.5 million obs and 70 features). I want to visualize the data in 2D, to look for naturally occurring clusters. Analogous to Van der Maaten's approach 1, I first reduce the ...
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1answer
554 views

Can I reconstruct the original data from a dimensionality reduced data using t-SNE?

I have implemented dimensionality reduction using PCA, AE, and t-SNE! I am trying to compare the output of the three methods by finding the reconstruction error. I have managed to reconstruct the data ...
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6k views

Are dimensionality reduction techniques useful in deep learning?

I have been working on Machine learning and noticed that most of the time, dimensionality reduction techniques like PCA and t-SNE are used in machine learning. But, i rarely noticed anyone doing it ...
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184 views

How is the family of distributions with PDF proportional to $(1+ax^2)^{-1/a}$ called?

Consider a family of distributions with PDF (up to a proportionality constant) given by $$p(x)\sim \frac{1}{(1+\alpha x^2)^{1/\alpha}}.$$ How is it called? If it does not have a name, how would you ...
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52 views

Visually distinct clouds, but stuck together

I have 96 features and the labels are represented by 1 and -1 for inputting to a deep learning model. 1- PCA Here the 3 axis represent the 3 first principal components. The blue cloud represents the ...