# Questions tagged [dimensionality-reduction]

Dimensionality reduction refers to techniques for reducing many variables into a smaller number while keeping as much information as possible. One prominent method is [tag pca]

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### Is there a particular order in which to do feature selection and sampling?

I want to use feature selection and observation subsampling on my data, for several reasons: feature selection for the usual motivations (reduce noise, decrease running time, etc.) observation ...
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### about high-dimensional regression data

I am doing experiments on the high-dimensional regression. However, it is hard to obtain the practical or synthetic high-dimensional data. I have checked on UCI website as well as some papers with ...
• 51
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### Feature selection with linear interaction between variables and correlation with categorical response variable

I am searching for a feature selection algorithm able to select the minimum number (minimum redundancy) of relevant variables (maximum relevance) with respect to a categorical response variable. I ...
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### Is t-SNE just for visualization?

I have used the t-SNE algorithm to visualize my high dimensional data. However, I was wondering if this is a practical method for inference?
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### Could principle component decomposed coordinates value be correlated to each other?

I am wondering if we have a A= n*p matrix of samples and we run a PC decomposition on it. Say the eigenvector matrix is E, so the samples in the eigenvector space ...
1 vote
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### Does PCA change the values of the data?

Principal Component Analysis is a means to reduce the dimensionality of data, if I understand correctly. So if I have a 1000 sample point 12 dimensional matrix and reduce it to a 1000 sample point 2 ...
3k views

### Is mutual information symmetric?

Why is mutual information symmetric, meaning why does I(A,B) = I(B,A)? Isnt the definition of mutual information, I(A,B), something like "the reduction of entropy in A when given B"? P(A|B) doesnt ...
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### Pruning and parameter reduction for decision trees

I am trying to perform a classification using a decision tree classifier. I was wondering whether using a Feature reduction method is relevant for decision trees since they automatically use pruning? ...
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### feature redundancy

Why exactly does features being dependent on each other, features having high correlation with one another, mean that they would be redundant? Also, does PCA help get rid of redundant/irrelevant ...
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### Principal components analysis with compositional data

Another beginner question: I'm trying to do PCA on compositional data. In other words, all the variables in the group add up to 100%. I've since learned on this forum that compositional data poses a ...
1 vote
600 views

### Compute angle of vector in word2vec models

If I understand correctly, the most_similar function computes the cosine similarity of the vector with all other vectors and finds the closest one. The vectors ...
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### Can I apply Clustering algorithms to the result of Manifold Visualization Methods?

Some methods related to manifold-learning are commonly stated as good-for-visualization, such as T-SNE and self-organizing-maps (SOM). I understand that when referring specifically to "visualization" ...
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### Can closer points be considered more similar in T-SNE visualization?

I understand from Hinton's paper that T-SNE does a good job in keeping local similarities and a decent job in preserving global structure (clusterization). However I'm not clear if points appearing ...
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### Free/open interactive softwares/plugins for end-users' high-dimensional data visualization

Aside from questions about How to visualize data of a multidimensional dataset (TIMIT), the Purpose of visualizing high dimensional data? and High-dimensional data: What are useful techniques to know?,...
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### How to reduce dimensionality of audio data that comes in form of matrices and vectors?

I'm working on a project involved with identifying different types of sounds (such as screams, singing, and bangs) from each other. We've got our data a reasonable number of different transformations ...
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### What is a good explanation of Non Negative Matrix Factorization?

I am trying to find a resource to understand non-negative matrix factorization. Apart from Wikipedia, I couldn't find anything useful.
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### How exactly dependent variable is expressed in terms of independent variables using Partial Least Square Regression Method?

I understand the working of NIPALS algorithm but while doing the regression using PLS how exactly the relation between known and unknown is established using Principle Component Analysis. The idea is ...
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### Improve the speed of t-sne implementation in python for huge data

I would like to do dimensionality reduction on nearly 1 million vectors each with 200 dimensions(doc2vec). I am using TSNE ...
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### Deciding about dimensionality reduction, classification and clustering?

Could you please help me to understand it because I'm not sure if I got it correctly. Let's say I have a dataset, of persons, with 100 features, various characteristics like height, weight, age, etc. ...
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### Purpose of visualizing high dimensional data?

There are many techniques for visualizing high dimension datasets, such as T-SNE, isomap, PCA, supervised PCA, etc. And we go through the motions of projecting the data down to a 2D or 3D space, so we ...
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### Completing MDS manually in R

Given a matrix A, I want to complete Multidimensional Scaling by hand, instead of using any given R functions. As such, I have calculated the centered matrix ...
1 vote
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### Various algorithms performance in a problem and what can be deduced about data and problem?

HI I am currently trying to apply various algorithms to a classification problem to assess which could be better and then try to fine tune the bests of the first approach. I am a beginner so I use ...
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### Dimensionality and Manifold

A commonly heard sentence in unsupervised Machine learning is High dimensional inputs typically live on or near a low dimensional manifold What is a dimension? What is a manifold? What is the ...
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### High-dimensional data: What are useful techniques to know?

Due to various curses of dimensionality, the accuracy and speed of many of the common predictive techniques degrade on high dimensional data. What are some of the most useful techniques/tricks/...
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### Scikit Learn: KMeans Clustering 3D data over a time period (dimentionality reduction?)

I have a dataset of xyz coordinates with a date component in a pandas dataframe ex: date1: $[x_1,y_1,z_1]$, date2: $[x_2,y_2,z_2]$, date3: $[x_3,y_3,z_3]$, .. I would like to classify a sample of ...
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### Reduction of multiple answers to single variable

The questionnaire for the data is here The first question takes multiple entry for the same question, I want to reduce this to a single variable. How do I do it? The clean data is available here. NB:...
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### Dimension reduction for logical arrays

I have measurements of 4 devices at two different points of time. A measurement basically consists of an array of ones and zeros corresponding to a bit value at the corresponding location: ...
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### machine learning algorithms for 2d data?

I'm looking for a supervised learning algorithm that can take 2d data for input and output. As an example of something similar to my data, consider a black image with some sparse white dots. Blur that ...
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### How to do SVD and PCA with big data?

I have a large set of data (about 8GB). I would like to use machine learning to analyze it. So, I think that I should use SVD then PCA to reduce the data dimensionality for efficiency. However, MATLAB ...
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### Can I use unsupervised learning followed by supervised learning?

I have a question about classifying documents using supervised learning and unsupervised learning. For example: - I have a bunch of documents talking about football. As we know, football has a ...
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### Nearest neighbors search for very high dimensional data

I have a big sparse matrix of users and items they like (in the order of 1M users and 100K items, with a very low level of sparsity). I'm exploring ways in which I could perform kNN search on it. ...
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