Questions tagged [pca]

Principal component analysis, a technique for dimensionality reduction.

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Should I apply PCA on the entire dataset or just the nominal values?

I have a data-set with 14~ attributes, roughly half of them nominal. I've used a binary vectorizer to convert these values to a number of attributes. The number of attributes, naturally, ballooned up; ...
870 views

Re-scale data after PCA for an LSTM?

I want to use the result of my PCA as an input for my LSTM model. I began by Applying the MinMaxScaler and then did the PCA, (then I reshaped my data of course) : ...
10k views

Why does PCA assume Gaussian Distribution?

From Jon Shlens's A Tutorial on Principal Component Analysis - version 1, page 7, section 4.5, II: The formalism of sufficient statistics captures the notion that the mean and the variance ...
48 views

PCA Reduction resulted in an elliptical form

I have a dataset with 19 features (columns). I normalized them using sklearn.preprocessing.normalize then I used PCA to reduce them to 2 components for plotting ...
162 views

How can I calculate Kernel matrix K for clustering based Kernel Principal Component Analysis?

In practice, a large data set leads to a large K, and storing K may become a problem. One way to deal with this is to perform clustering on the dataset, and populate the kernel with the means of those ...
6k views

What is the difference between observation and variable?

I have a matrix with size m×n that is built from n number of individuals for person identification. So, n is the number of person and m is the number of feature's value for the person. It makes me ...
122 views

How to test trained PCA used for compression?

I am working on an exercise for using PCA for compression of images and I don't quite understand how to use it on the test data: I have 300 images of hand drawn sixes, represented by 28x28 matrices, ...
792 views

Assessing Group Similarities and Dissimilarities Post PCA

The goal is to assess similarity and dissimilarity between 6 known groups. The original data began with the 6 known groups and 2,700+ variables all on a scale of 0 to 100. I have performed PCA to ...
142 views

Dimensionality reduction with prior knowledge of colinearity between features

Let's say that I have sparse feature vectors and I'd like to use dimensionality reduction in order to visualize them more easily. Dimensionality reduction techniques like PCA will estimate ...
142 views

stable set PCA while adding features

Is it possible to have a PCA setup (or any other dimensionality reduction technique) in a way that adding new features wouldn't require retrain downstream models that were trained on that particular ...
249 views

Best ML technique to suggest predictor variables

In the following dataset, the first 4 columns are predictor variables and the engine running index is the response variable. ...
205 views

Why is my PCA boomerang-shaped when normalizing?

Running an unsupervised plot of my data, I noticed a hyperbolic ('boomerang') shape: ...
6k views

tsne for prediction

I have a traditional prediction setting, with a training data set train and a test data set test. I do not know the outcome <...
124 views

What are possible approaches to deal with unseparable data even after PCA?

Greetings data scientists, I am dealing with a complex classification/prediction problem and I am finding very difficult to separate the classes at all. Even after PCA, my data (over the two PCs), ...
3k views

What is PCA and MICE

I am doing an experiment on Azure ML. While pre processing my data, there is an option to clean missing data using either PCA or MICE. Please provide me an example of how I can decide on which option ...
114 views

Could data from a test set 'leak' into predictor during PCA?

After reading this article I have got a question about PCA. Author was talking about whether to use test set while computing PCA. But, few important points to understand: 1) We should not ...
493 views

Dimensionality reduction with PCA limitations

What are the cases when we should not use PCA for dimensionality reduction and what to use in such cases?
2k views

What happens when you have highly correlated columns in a dataset?

I am doing a regression model. And I was wondering what would be the consequence if we have two or more Highly correlated ...
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Classify multivariate time series

I have a set of data composed of time series (8 points) with about 40 dimensions (so each time series is 8 by 40). The corresponding ouput (the possible outcomes for the categories ) is eitheir 0 or 1....
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What can i do after a PCA with the results?

After performing a PCA and studying the proceeding i ask myself what the result is good for in the next step. From the PCA i learned how to visualize the dataset by lowering the dimension, i got handy ...
190 views

Anomoly detection method selection

I need to decide between SVM (One-Class Support Vector Machine) and PCA (PCA-Based Anomaly Detection) as anomaly detection methods. Azure ML is used and provides SVM and PCA as methods - hence the ...
855 views

Understanding how distributed PCA works

As part of big data analysis project, I'm working on, I need to perform PCA on some data, using cloud computing system. In my case, I'm using Amazon EMR for the job and Spark in particular. Leaving ...
174 views

I have n dimensional data and I want to check integrity, can I downgrade to 2 dimensional feature space via PCA and do so?

Say I have n dimensional data samples. I want to check the integrity of the features, if they are good representation of the respective classes, i.e. these features are good or not. My plan is: I ...
326 views

How is PCA is different from SubSpace clustering and how do we extract variables responsible for the first PCA component?

New update: I understand PCA components ensure we select variables responsible for high variance, but I would like to know how to extract key variables responsible only for high variance through PCA ...
246 views

How to Interpret the output of PCA?

I have dataset of 50000 values (rows) and 1000 variables (columns). Since this is high dimensional, I am unable to work with just DBSCAN. So I am trying to use PCA (principle component analysis). ...
1k views

PCA on Neural Networks dimensions reduction? [closed]

The dataset which was extracted from the database consists of more than 50 columns, I call these columns dimensions, can I call them dimensions? Obviously, I have to do dimension reduction on ...
6k views

Is Overfitting a problem in Unsupervised learning?

I come to this question as I read the use of PCA to reduce overfitting is a bad practice. That is because PCA does not consider labels/output classes and so Regularization is always preferred. That ...
10k views

PCA before K-mean clustering

If I applied PCA on feature vectors and then I do clustering, such like following: ...
424 views

Examples for predict.FAMD?

I am doing a study on unsupervised data with various categorical variables. So I have found the FactoMineR package to be really handy for this, particularly with the FAMD functions. I can get to a ...
179 views

predict rank from physical measurements with various lengths

I have physical measurements with length 2*n, where the first vector represents a charge or a capacity (in Coulomb) $C$ and the second one is a voltage $V$. Let's call this measurement "forming". A ...
302 views

What are some methods for clustering individuals into distinct groups based on Features?

I started with a dataset that contained many dimensions for individuals (each id is a separate individual), and extracted three Features/Attributes columns for each ...
200 views

Principal Component Analysis, Eigenvectors lying in the span of the observed data points?

I have been reading several papers and articles related to Principal Component Analysis (PCA) and in some of them, there is one step which is quite unclear to me (in particular (3) in [Schölkopf 1996])...
163 views

PCA on matrix with large M and N

Based on this answer, we know that we can perform build covariance matrix incrementally when there are too many observations, whereas we can perform randomised SVD when there are too many variables. ...
548 views

EOF/PCA/MCA Analysis for a set of data

I have a set of climate data (temperature, pressure and moisture for example), $X$, $Y$, $Z$ which are matricies with dimensions $n \times p$ where $n$ is the number of observations and $p$ is the ...
531 views

How to evaluate the quality of representation for variables and individuals of a PCA in scikit-learn?

I just looked at the PCA in scikit-learn, but I didn't find a way to evaluate the quality of representation for variables and individuals like I usually do using the squared cosine. The squared ...
691 views

Why training error is larger than validation error after PCA?

We have 4000 features and we are applying Principal Component Analysis to reduce them a small number of features from 20 to 100. We are performing linear regression. Both training and validation ...
4k views

Imagine I've the following matrix, which gives the grades of students in the subjects German, Philosophy, Math and Physics: ...
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Multidimensional Scaling with Categorical Data

I have read the following about MDS in a book: using MDS requires an understanding of the individual feature's units; maybe we are using features that cannot be compared using the Euclidean ...
318 views

PCA algorithm problems - Python

I have implemented PCA algorithm and I understood it very well but still I have some questions. My code is below and it's very simple implementation. ...
223 views

Looking for an algorithm that correctly clusters visually separable clusters

I have visualized a dataset in 2D after employing PCA. As 2D visualization shows in figure, there is a good separation between points (A, B). Now, I want to use a metric which can separate these ...
2k views

Which variables matter most for prediction of another variable?

I have a dataset and need to predict, out of 9 variables, which ones matter most to predict number 10. I first tried using the selectKBestmethod from ...
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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 ...
2k views

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 ...
2k views

How can give weight to feature before PCA

I wonder how can I give weight to my feature before employing PCA. I mean somehow weighted PCA. Because I know that one of the features is better than others and want to give importance to it in ...
2k views

Converting non-numeric data values into equivalent rank scores

Consider a data-frame similar to the one shown (the actual data-frame is much larger) ...
2k views

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 ...
565 views

Feature Selection and PCA

I have a classification problem. I want to reduce number of features to 4 (I have 30). I'm wondering why I get better result in classification when I use correlation based feature selection(cfs) first ...
6k views

How do I make an interactive PCA scatterplot in Python?

The matplotlib library is very capable but lacks interactiveness, especially inside Jupyter Notebook. I would like a good offline plotting tool like plot.ly.