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Questions tagged [dimensionality-reduction]

Refers to techniques for reducing a large number of variables or dimensions spanned by data to a smaller number of dimensions while preserving as much information about the data as possible. Prominent methods include PCA, MDS, Isomap, etc. The two main subclasses of techniques: feature extraction and feature selection.

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using latent dirichlet allocation to reduce the number of dimensions in bag of words model?

Does anyone have experience reducing the dimensions in a traditional bag of words model? For example, if you want to train a decision tree on a large set of reviews, the size of the vocabulary ...
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Clustering users with very sparse data

I have a dataframe of the form ...
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Positioning multivariate data in a 2-dimensional space (with PCA)

I have multidimensional data. (11 columns - attributes , 150K rows - number of data). It is slightly sparse-alike data, for example, which means one datum has numeric values like (0, 0, 6.5, 0, 0, 7.5,...
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How to conduct a principal component analysis on data set with large number of zeros

I have data for percentage cover of plant species in 500 sites. There are columns for 30 different species in the data set and I would like to drastically reduce this down to a manageable number of ...
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Chi Square Test for Dimensionality Reduction

According to many resources, we should have categorical variable to be able to apply chi square test. ...
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Dimensionality Reduction - Feature Selection

For example, we have a dataset in which the samples contain 400 features. In this case, if we try to perform classification, we get very low accuracy because our learning model will become very ...
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Appropriate dimensionality reduction technique for a small, but high-dimensional sample

I am attempting to conduct some multivariate analysis on a dataset I've been given with a sample size (n) of 23 and a feature number (p) of ~800. I would like to use dimensionality reduction, but ...
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The best number of nodes in bottleneck layer in Autoencoder

I would like to perform dimensionality reduction using autoencoders (similar to PCA) and I am not sure how many components are optimal i.e. what should be the size of the bottleneck layer. I was ...
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How can Item Response Theory be used to remove questions asked in a customer satisfaction survey?

I have results from a survey of around 30 Likert-style questions that are asked of customers on their opinion about company X. Each of the 30 questions belongs to a certain category. For example, ...
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Principal Component Analysis - Why Use Eigenvectors of the covariance matrix? [duplicate]

In PCA we start with a dataset and we reduce its dimensions by giving it new features that are each a linear combination of the original features of the dataset, and only keeping the ones with maximum ...
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Predicting user behaviour based on transactional data - flagging “risky” behaviour

Firstly, I'm not sure if this is the right instance of StackOverflow to post on so feel free to ask me to put it elsewhere. I am exploring the concepts of clustering and "unsupervised" learning for ...
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When to use PCA vs LDS vs nMDS for microbiota dataset?

I'm trying to understand the certain situations in which you would use the 3 above ordinance/rank tests over the other in terms of microbiota count data. Typically, I have been told to use nMDS over ...
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Correct way to calculate MSE for autoencoders with batch-training

Suppose you have a network representing an autoencoder (AE). Let's assume it has 90 inputs/outputs. I want to batch-train it with batches of size 100. I will denote my input with ...
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Finding optimal subspace for Linear Discriminant Analysis - Elements of Statistical Learning 4.3.3

Linear Discriminant Analysis (LDA) possibly operates a dimension reduction. Section 4.3.3 in Elements of Statistical Learning explicits this notion as well as a method for computing the "optimal ...
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Generating new samples from dataset to expand dataset

I want to choose one dataset and then expand the dimensions/number of samples to show how a dimension reduction method(not yet decided) reacts to changes in dimensions/number of samples. My plan was ...
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How can Factor Analysis be used to remove questions from a survey?

Suppose I have a psychological questionnaire asking 30 questions about a person's mental health (on a Likert-scale 1-7). These 30 questions fall into 7 separate, but correlated categories. The ...
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Simplification of bivariate normal $\phi_2(x,y,\rho)$ at $y=y_F$ (i.e. fixing one of the axes)

Suppose we start off with the traditional standard bivariate normal distribution: $$\phi_2(x,y|\rho,\mu_x=0,\mu_y=0,\sigma_x=1,\sigma_y=1)=\frac{1}{2\pi\sqrt{1-\rho^2}}\exp \left(-\frac{x^2-2\rho x y ...
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How to identify and reduce question overlap and redundancies in a survey? (remove questions asked for a more concise survey, w/o losing information)

Suppose I have a survey that contains 30 items. The items ask about the relationship between the respondent and their family, in many different realms. For example, the strength of the connection ...
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Dimensionality reduction before clustering cosine data values causes a change of scale

In my experiment, I am doing hierarchical agglomerative clustering of texts (parameters: cosine, average). My features matrix is very sparse, so I considered PCA as dimensionality reduction technique. ...
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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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Dimension Reduction on Data with both Spatial and Non-Spatial Variables to Train a Logistic Regressor for Cross Sectional Time Series Data

I need some help on how to process and analyse a study of mine. I'm running a study on mice to look at the effect of diet on cells over a series of time. My mice are divided into two groups, one group ...
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How do you actually use PCA in MATLAB?

I'm trying to use the pca command in MATLAB for dimensionality reduction. I know that [U, V] = pca(X) will yield the principal components in U and the scores in V,...
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What to do after feature agglomeration in Python?

I'm attempting to use FeatureAgglomeration (sklearn) package in Python to reduce the dimensionality in my dataset (which contains many collinear variables). I haven't been able to find as many ...
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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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Are training-loss optimised embeddings useless? (help resolve a disagreement)

The aim We are training a feed forward neural network as a regressor, with the aim of using the activations of the final layer as a type of embedding vector to represent the input examples. The ...
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Similar plots of 2 closely related data sets

Suppose my data set contains 1000 observations and 10 variables. I consider 2 data sets: the original one and the one which contains only the first 5 variables (the number of observations is still the ...
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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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Help with terminology and/or method: find parameters that minimize cost function for each cluster of initial conditions?

This is a bit embarrassing but I can't even find the words to properly do a Google search. I am doing simulations and I am trying to develop a better adaptation process. The process has seven ...
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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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Principal Components' relation with variables having lower variance

This is a philosophical question about PCA, and not a direct coding question. I understand that PCA is a dimensionality reduction technique which results in a certain set of PCs, each PC being a ...
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Why do I get an error with this data using principal axis factoring but not minimal residual factoring?

I am using n_factors() from the "psycho" package in R to figure out the number of factors for a set of data. When I use prinicipal axis factoring I get the following error: ...
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How can I recover full dimensional VAR model coefficients after fitting a VAR model to a dimensionality reduced (via PCA) dataset?

I am using PCA to reduce dimensionality prior to fitting a multivariate time-series dataset to a VAR (vector autoregressive) model. Is there any way to convert a PCA-derived VAR model to a full ...
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Factor Analysis: Single variable contributing to several latent variables

I was wondering whether factor analysis is right tool in my scenario. That is, I have dataset $X = (X_1, X_2, X_3, X_4)$, where $X_i$ denotes a single variable. As far as I understand factor analysis, ...
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Dimensionality Reduction for Optimally Preserving KNN

Do any dimensionality reduction techniques find embeddings which optimally preserve the K-nearest neighbors of each point? If no algorithm provably does this, are there algorithms which heuristically ...
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Combining subjoint distributions to create a larger joint distribution

I am trying to construct large joint distributions through smaller joint distributions and I'm not sure how to approach the literature. I am curious if there exists a function which can take n ...
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93 views

Dimension Reduction for mixed variables

I am working with a dataset which consists of both categorical (14 vars) and continuous variables (5). Each categorical variable consists of a minimum of 2 categories up to 106 categories. The aim ...
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Number of factors in Factor Analysis of Mixed Data with FactoMineR

I'm trying to perform FAMD with FactoMineR because I want to reduce the dimensionality of my data. My data has 378 dimensions and 34K rows. Around 350 of those dimensions are categorical and the rest ...
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Supervised machine learning for dimensionality reduction of control variables in logistic regression

Is it a valid approach to use the predictions of a supervised machine learning (ML) algorithm as a form of dimensionality reduction of control variables in the context of logistic regression? ...
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Approaches to reduce dimensions (feature selection/extraction) with high dimensional count data before running tree based model

My dataset has ~100k samples and 3000 dimensions. The data are counts, anywhere between 0-8 and it's pretty sparse. Because of 'curse of high dimension', I want to shrink the number of variables ...
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159 views

How to interpret PCA coefficients to reduce dimension

I have read about similar questions. I have data which has 68 columns and about 800 samples. The 68. column is the output the rest 67 is the input variables. I want to reduce the size of my input ...
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19 views

Linear Discriminant Analysis vocabulary question

I am doing a descriptive LDA on a dataset with two (known, easily separable) classes and many features (and many more observations). I intend to use the latent variable values as a dimensionally-...
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26 views

Is the grid in a self organizing map static?

I'm trying to write my own SOM in python, and after reading material from several sources (and watching video tutorials) I think I understand all the steps. There is however one issue that I want to ...
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Clarification on quantification of Categorical variables

I have a countries column with 49 levels. I want to quantify it. If I run CATPCA on that column would i be able to get the quantified result. Since CatPCA is like PCA or factor analysis: it extracts ...
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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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Is sketching a method for dimensionality reduction and its relation to random projection

I want to know if sketching can be categorized as a method of dimensionality reduction and more specifically feature extraction. Also, i want to understand if its related to random projection.
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Is it possible to weight items differently in a factor analysis?

Suppose I have 100 targets that have been rated by 1000 individuals. I want to perform a PCA on those 100 targets. Now, I'm curious if I were to take some property of the targets into account, how ...
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28 views

Similarity measure before and after dimensionality reduction or clustering

I have a dataset with 500 000 samples, each sample contains 30 features. The values of the features are in the range 0.0 to 1.0. ...
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1answer
165 views

advantage of variational autoencoder

I know that VAE is generative model. However it is also used as a dimensionality reduction method. In this case, what are advantages of VAE?? Also I saw that well-applied vae on mnist, and it was ...
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Clustering and regression with high dimensional, mixed type data

I have been looking at several similar questions and answers discussing these issues but I cannot say there is a clear answer to what I am posting here. There seems to be a general confusion with the ...
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Reference point in projection axis of SVD (singular value decomposition)

I am watching a YouTube video on SVD, and attempting to recreate some of its examples to better understand the internal machinery of the algorithm. In one of the slides, the instructor mentions that ...