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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138 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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43 views

Clustering before/after t-SNE?

My dataset contains 100 features about travel behaviors of ~500 users, created using doc2vec. I want to create clusters of these users and I'm planning to use either k-Means or DBSCAN algorithm. My ...
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137 views

Force directed graphs vs. diffusion maps vs. other dimensionality reduction methods

Can you help me with a conceptual explanation of how force directed graph drawings work compared to other methods in the context of dimensionality reduction for visualization purposes? In particular, ...
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40 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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10 views

Visualization and two-group statistical testing in high-dimension binary data

I am planning a biomarker discovery experiment, and the data is expected to take the form of a high-dimensional dataset where each data point is described by 8 binary variables indicating the presence ...
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2answers
167 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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17 views

How should multiple *continuous* features on an embedding plot like UMAP be depicted?

For the MNIST digits dataset, it is pretty straightforward to colour the various points of its UMAP/t-SNE projection. For continuous values of features, it is less obvious to me. For a case of, say, ...
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45 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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43 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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34 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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40 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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174 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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186 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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194 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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34 views

Does a close clustered t-SNE imply bad features?

I'm playing around with machine learning and plotted the feature vector (200 dimensions, 7000 samples, 2000 red, 5000 green) using t-SNE. As you can see, the plot does not really produce two (or more) ...
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12k views

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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1k 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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2answers
949 views

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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1answer
914 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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102 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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455 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
353 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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168 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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154 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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289 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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57 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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353 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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362 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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317 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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1answer
240 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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56 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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1answer
391 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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2answers
96 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
197 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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542 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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1answer
508 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
532 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
370 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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3answers
4k 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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175 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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1answer
49 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 ...
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613 views

Why Kullback-Leibler in Stochastic Neighbor Embedding

Stochastic Neighbor Embedding (and t-SNE) relies on Kullback-Leibler divergence between the point distributions in the original and the low-dimensional space. Why? Why not any other dissimilarity ...
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28 views

Developing novel association measure with t-SNE

I am an MSc. student in computing and I am currently writing my thesis on radiogenomics (imaging genomics) for brain cancer research. I NEED YOUR HELP. My supervisor and I are thinking about ...
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1answer
82 views

The usage of “variance” (vs. “standard deviation”) in the 2008 t-SNE paper

In the t-SNE paper from 2008 the term "variance" is mentioned on several occasions. For example on page 3: For nearby datapoints, $p_{j|i}$ is relatively high, whereas for widely separated ...
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2answers
2k views

How to do dimension reduction with sparse data

I have 200 vectors representing the percentage marks for 200 different students in the different classes they took. The vectors are 22 dimensional (as there were 22 different classes in total) even ...
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1answer
2k views

Does it make sense to run DBSCAN on the output from t-SNE? [duplicate]

Performed time series clustering where I used DTW to generate a distance matrix. The distance matrix was then given as an input to t-SNE where the two-dimensional results from t-SNE were used for ...
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1answer
2k views

What is the meaning of the axes in t-SNE?

I'm currently trying to wrap my head around the t-SNE math. Unfortunately, there is still one question I can't answer satisfactorily: What is the actual meaning of the axes in a t-SNE graph? If I were ...
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1answer
79 views

What is meant by: neighbor identities are preserved in t-SNE?

From the article on Stochastic Neighbor Embedding (by Geoffrey Hinton and Sam Roweis): Stochastic Neighbor Embedding (SNE) tries to place the objects in a low-dimensional space so as to ...
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54 views

Interpretability of the dimensions in t-SNE [duplicate]

I'm relatively new to t-SNE and it seems that unlike in other dimensionality-reduction techniques, the dimensions in t-SNE are hard to interpret. The contribution of the variables can easily be ...
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1answer
1k views

t-SNE, PCA and another technique - which one? [closed]

I am comparing dimension reduction techniques and I am utilizing them for data visualizations onto a plane – projections in 2D space. The input into a projection/dimension reduction techniques is a ...