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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What is best practise for dimensionality reduction in rows of data

I was wondering what was best practise for dimensionality reduction in observations (as opposed to features) in a data-set? I often have data comprising of a multiple, random number of observations ...
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25 views

Can I apply factor analysis on multiple choice questions?

I am looking to validate a questionnaire and would like to know if I can use factor analysis on the multiple-choice questions (MCQ). Also, I have another section where I am asking about participants ...
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When should I use feature selection and when should I use dimensionality reduction techniques?

When should I use feature selection and dimensionality reduction? I know that feature selection is different from dimensionality reduction. But I don't know under what circumstances should I use ...
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34 views

Model Selection and RFE using caret

I'm faced with a high dimensional (samples=148, features=20000), supervised binary classification problem. Which I would like to approach with an ensemble of classifiers, that will classify using a ...
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40 views

In PCA, can the values in the principle component vectors which are close to zero be removed to see the important features? [duplicate]

In PCA, when I extract the principle component vectors, I am choosing the first vector with the largest corresponding eigenvalue. I notice that some of the values in this vector are close to zero. Can ...
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36 views

Dimension reduction for discrete qualitative and aggregated variables

I know about PCA for multiple dimensions of continuous features but here is a problem I have some trouble to find a method for. I don't have a list of individual countries but rather a discrete ...
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36 views

Support Vector Machines and the curse of dimensionality

I am reading this paper: "Automated MR image classification in temporal lobe epilepsy", by Focke et al. NeuroImage, 2012. The authors use support vector machines to classify subjects between healthy ...
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22 views

Are there any versions of t-SNE for streaming data?

My understanding of t-SNE and the Barnes-Hut approximation is that all data points are required so that all force interactions can be calculated at the same time and each point can be adjusted in the ...
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26 views

In R, is there an exact method for the political compass test?

In R, I am looking, in an exact way, for the method (or packages) used on the political compass test (see politicalcompass.org in the link Take the test) In my case, I have one data set consisting in ...
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69 views

What is the difference between feature selection and dimensionality reduction?

I know that both feature selection and dimensionality reduction aim towards reducing the number of features in the original set of features. What is the exact difference between the two if we are ...
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17 views

PCA reduction and low-reliability components

I'm working on a survey with 288 observation in total (108 complete answers used) and around 200 variables. I'm working on reducing those number using Principal Components Analysis, using R. Suppose ...
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65 views

How to reduce dimension of the sampling procedure?

I am stuck with this problem for a long time, hopefully I can get help here! Basically, I want to sample from a posterior distribution that looks like, \begin{align*} X &\sim ...
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30 views

LDA vs. SVM for Dimensionality Reduction

Whats the difference between LDA and Linear SVM for dimensionality reduction. I am little confuse as LDA also looks for projection that separates the classes of data and SVM we also look for ...
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27 views

Feature space reduction for tag prediction

[x-post] from stackoverflow. I am writing a ML module (python) to predict tags for a stackoverflow question (tag + body). My corpus is of around 5 million questions with title, body and tags for ...
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33 views

technical issues regarding to cluster analysis

Hi I would like to seek help with my cluster analysis using SAS. The main objective of the task is to segment customers into groups based on their similarity. The dataset contain mixed types of ...
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1answer
28 views

Intrinsic dimensionality estimation using Laplacian Eigenmaps

I learnt that I can look at the eigenspectrum of the kernel matrices computed by nonlinear spectral techniques in order to estimate the intrinsic dimensionality of a data-set. I use drtoolbox (The ...
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45 views

pruning Neural Network

Since a feedforward NN with a logistic function as activation function is not linear, does it make sense to reduce variables first with principal components or discriminant analysis? Because ...
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30 views

Clustering Consumer data with over 100 variables and 50000 rows each

I am tasked with performing a clustering exercise for a consumer survey dataset with the hopes of finding distinct consumer segments. In the past, I've done it using a variety of techniques- ...
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15 views

Dealing with seasonality when doing dimensionality reduction

I want to perform dimensionality reduction (in particular, PCA) on a data set that is highly seasonal. One approach that I came across when researching this is "seasonal PCA", where you split your ...
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591 views

Relationship between SVD and PCA. How to use SVD to perform PCA?

Principal component analysis (PCA) is usually explained via an eigen-decomposition of the covariance matrix. However, it can also be performed via singular value decomposition (SVD) of the data matrix ...
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62 views

How does CCA find a low-dimensional common subspace?

According to Wikipedia, canonical correlation analysis (CCA) finds pairs of canonical variables. CCA has also been used in many cases as dimensionality reduction tool to find low-dimensional ...
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1answer
150 views

Combining several variables into one outcome score: How is it done in the machine learning community?

I have got 8 cognitive (continuous) behaviour variables and would like to combine them into a composite score. I would then like to find the best predictors of this outcome (from about 50 predictors). ...
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66 views

How many components to use in PCA in order to preserve a certain amount of variance?

I want to reduce the dimensionality of my data with PCA, until it preserves $\alpha = 0.99$ of the variance. How do I decide how many eigenvectors I should use? So I'm looking for a function ...
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33 views

Reduction of species variables in vegetative analysis

Edited following helpful feedback. I have vegetation species data for a number of grassland habitat sites, and am preparing to begin Exploratory Data Analysis. Data was collected in 100 quadrats over ...
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When is t-SNE misleading?

Quoting from one of the authors: t-Distributed Stochastic Neighbor Embedding (t-SNE) is a (prize-winning) technique for dimensionality reduction that is particularly well suited for the ...
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Dimensionality reduction technique similar to LDA when class labels are probabilistic

Given discrete class labels, say True and False, LDA (linear discriminant analysis) can be used to perform discriminant dimensionality reduction and attempt to find a subspace that best separates the ...
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Fourier vs ARIMA vs Factor analysis vs PCA?

Background I'm currently analysing a timeseries. My data consists of half hourly observations of a certain measurement. This data is human generated, and so we believe there will be daily, or weekly, ...
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109 views

How to choose a kernel for kernel PCA?

What are the ways to choose what kernel would result in good data separation in the final data output by kernel PCA (principal component analysis), and what are the ways to optimize parameters of the ...
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62 views

What exactly is the procedure to compute principal components in kernel PCA?

In kernel PCA (principal component analysis) you first choose a desired kernel, use it to find your $K$ matrix, center the feature space via the $K$ matrix, find its eigenvalues and eigenvectors, then ...
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Derived Scale for Exploratory Factor Analysis

Is it appropriate to use a 10 point Likert scale for EFA in the form of Yes/No followed by strength of the response varying from Very Low to Very High? The scores would be assigned as follows Yes/VL ...
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Is there any value in dimensionality reduction of a data set where all variables are approximately orthogonal?

Suppose I have an $N$-dimensional data set where the $N$ dimensions are roughly orthogonal (have correlation zero). Is there any utility in terms of: Visualization Representation (for classifier ...
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79 views

Dimensionality reduction when number of samples is much larger than number of features

I was wondering what happens when the number of samples is much larger (e.g. $\times 200\:000$ times more) than the number of features? Is there any recommended way of reducing the samples' ...
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34 views

Reducing the number of independent variables [duplicate]

I am working on a research project and I am trying to find one variable (execution time) as a function of a number of other variables/ metrics which were logged during the execution of a job. There ...
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93 views

How to perform PCA in Matlab when number of dimensions is larger than number of observations?

I have a data matrix of say, $3000 \times 200$, i.e. I have $3000$-dimensional observations from $200$ subjects. How can I reduce the dimensionality to $1000$ in MATLAB? With bigger numbers, ...
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46 views

How to verify implementation of SVD in Javascript

I have implemented the SVD algortihm for my Node.js project for collaborative filtering of a sparse dataset based on this paper by GroupLens. For calculating the SVD, I am using the package node-svd ...
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103 views

How to build a predictive model with a billion of sparse features?

I am making a model to learn a dataset which has a big feature number and sparse samples (I am planning to use logistic regression). The feature number can be as big as 1,000,000,000. It is sparse ...
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Alternative methods of data reduction

There is obviously a lot of discussion on this site on appropriate data reduction techniques, particularly between factor analysis and principal component analysis. Soon, I will need to begin ...
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58 views

Is it possible to project a new vector onto the PC space using kernel PCA?

Let $X_{N \times d}$ be the data matrix, where $N$ is the number of samples and $d$ the size of the features space. Using kernel PCA (kPCA), one first computes a kernel matrix $K_{N \times N}$, and ...
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24 views

Using ANOVA to reduce number of variables

I am interested in using a machine learning technique (kriging) to model a function, based on a large number of variables. The large number of variables are causing overfitting problems, and I would ...
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137 views

A person repeatedly selects the two most similar items out of three. How to model/estimate a perceptual distance between the items?

A person is given three items, say pictures of faces, and is asked to pick out which two of the three faces are the most similar. This is repeated a large number of times with different combinations ...
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593 views

How to understand “nonlinear” as in “nonlinear dimensionality reduction”?

I am trying to understand the differences between the linear dimensionality reduction methods (e.g., PCA) and the nonlinear ones (e.g., Isomap). I cannot quite understand what the (non)linearity ...
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Using SVD on features before SVM classification, when p >> N

So I am going through Hastie's Elements of Statistical computing, and in section 18.3.5 which deals with computational shortcuts when the number of dimensions $p$ is much larger that the number of ...
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121 views

Why are there only $n-1$ principal components for $n$ data points if the number of dimensions is larger than $n$?

In PCA, when the number of dimensions $d$ is much much greater than the number of samples $N$, why is it that you will have at most $N-1$ non-zero eigenvectors? In other words, the rank of the ...
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Clustering cases on variables discovered in-sample via factor analysis?

My Data I have 2-hourly readings on approximately 10K sensors taken over the course of a year. The resulting time series look pretty similar day to day (though there are some longer term trends), and ...
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178 views

Why are mixed data a problem for euclidean-based clustering algorithms?

Most classical clustering and dimensionality reduction algorithms (hierarchical clustering, principal component analysis, k-means, self-organizing maps...) are designed specifically for numeric data, ...
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Motive behind preserving variance

Dimensionality reduction techniques preserve some properties of the data. I was wondering how preserving variance (as PCA does) can be helpful? Precisely speaking, PCA takes the covariance matrix and ...
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2k views

What is the difference between ZCA whitening and PCA whitening?

I am confused about ZCA whitening and normal whitening (which is obtained by dividing principal components by the square roots of PCA eigenvalues). As far as I know, $$\mathbf x_\mathrm{ZCAwhite} = ...
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24 views

Reduction of sparse features for machine learning

I'm trying to use a 1D histogram as a feature for machine learning. A histogram instance can be very sparse and the range of its bins is theoretically unbounded. Moreover it is expected that non-zero ...
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91 views

How can there be a linear correlation between two PCA components?

I perform principal component analysis (PCA) on a dataset, and then plot the first and the second principal components. I get the following phenomenon: one principal component appears to be a linear ...
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245 views

More recognizable Python implementation of Linear Discriminant Analysis?

I have been using scikit-learn's LDA implementation to do some experiments, and recently wanted to test out some modifications to the LDA derivation. I was looking at the Python implementation that ...