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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 ...
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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 ...
gc5's user avatar
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9 votes
4 answers
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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?
smw's user avatar
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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 ...
user3113633's user avatar
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4 answers
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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 ...
MyBushisaNeonJungle's user avatar
6 votes
2 answers
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 ...
Armon Safai's user avatar
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1 answer
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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? ...
Mehdi's user avatar
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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 ...
Armon Safai's user avatar
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2 answers
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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 ...
Mark Green's user avatar
1 vote
0 answers
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 ...
Javierfdr's user avatar
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2 votes
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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?,...
Laurent Duval's user avatar
4 votes
2 answers
712 views

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 ...
Ben Sandeen's user avatar
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1 answer
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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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1 answer
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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 ...
Shaleen Jain's user avatar
28 votes
5 answers
22k views

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 ...
chmodsss's user avatar
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2 votes
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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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30 votes
8 answers
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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 ...
hlin117's user avatar
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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 ...
potatosoup's user avatar
1 vote
1 answer
73 views

Optimal projection for data visualization

I have 90k points in $\mathbb{R}^{32}$ (i.e., a 90k by 32 real matrix) which I want to visualize. I know I can cluster my points (k-means &c), but I want to select a few "interesting" 2-planes in $...
sds's user avatar
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3 votes
1 answer
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Online/incremental unsupervised dimensionality reduction for use with classification for event prediction

Consider the application: We have a set of users and items. Users can perform different action types (think browsing, clicking, upvoting etc.) on different items. Users and items accumulate a "...
JohnnyM's user avatar
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1 answer
506 views

Illustrating the dimensionality reduction done by a classification or regression model

Tl;DR: You can predict something, but how do you explain the prediction? EDIT: I have built a website that tries to answer this question with means of embedding / visually clustering data according ...
BenoitParis's user avatar
1 vote
2 answers
280 views

Prepare data for SVM, Is it valid to normalise the data before and after PCA dimension reduction

Is it valid to normalise a dataset, reduce dimensionality with PCA and then to normalise the reduced dimension data. Assuming this is performed on training data, should the same PCA coefficients be ...
Michael's user avatar
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10 votes
2 answers
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Reducing the dimensionality of word embeddings

I trained word embeddings with 300 dimensions. Now, I would like to have word embeddings with 50 dimensions: is it better to retrain the word embeddings with 50 dimensions, or can I use some ...
Franck Dernoncourt's user avatar
4 votes
3 answers
206 views

Is it ok to interprete PCA plot this way?

I have several samples (C2, C4, C5) and want to check if they are at a certain stage. I included some known samples (D0 - D77) which were generated at different stages by another lab. In the PCA plot, ...
Dejian's user avatar
  • 141
7 votes
1 answer
142 views

Projecting data from $S^n$ to $S^2$

I have few points in $S^n$, i.e., the $n$-dimensional unit sphere embedded in $\mathbb{R}^{n+1}$, and I would like to project them down to $S^2$, i.e., the 2-dimensional unit sphere (embedded in $\...
Pratik's user avatar
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3 votes
0 answers
77 views

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 ...
Ando Jurai's user avatar
23 votes
4 answers
17k views

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 ...
alvas's user avatar
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17 votes
2 answers
11k views

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/...
ASX's user avatar
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4 votes
2 answers
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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 ...
flow's user avatar
  • 141
4 votes
2 answers
139 views

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:...
Ankit Haldar's user avatar
3 votes
1 answer
159 views

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: ...
user1192748's user avatar
4 votes
2 answers
5k views

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 ...
Brannon's user avatar
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36 votes
6 answers
33k views

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 ...
David S.'s user avatar
  • 547
5 votes
4 answers
10k views

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 ...
Ali's user avatar
  • 361
23 votes
3 answers
9k views

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. ...
cjauvin's user avatar
  • 451
70 votes
11 answers
40k views

What is dimensionality reduction? What is the difference between feature selection and extraction?

From wikipedia: dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature ...
alvas's user avatar
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28 votes
6 answers
3k views

Machine learning techniques for estimating users' age based on Facebook sites they like

I have a database from my Facebook application and I am trying to use machine learning to estimate users' age based on what Facebook sites they like. There are three crucial characteristics of my ...
Wojciech Walczak's user avatar

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