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Questions tagged [pca]

Principal component analysis, a technique for dimensionality reduction.

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Whether to use LDA or QDA

I'm trying to determine whether it's best to use linear or quadratic discriminant analysis for an analysis that I'm working on. It's my understanding that one of the motivations for using QDA over LDA ...
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Hard time finding literature on feature clustering using Principal Component Analysis

Im new to StackExchange, so i am sorry if this is not the right way to ask a question on StackExhange. For my thesis I wish to propose a methode for future research on using PCA to cluster features (...
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How to interpret scikitlearn pca components output

I am trying to use PCA with scikitlearn for feature selection and there is something about PCA that I am not understanding. Can someone please fill in the blanks for me? I have a normalised dataset ...
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Eigenvectors of points on a straight line PCA1 and PCA2

let immagine that I have 3 points and they are all on a sloped straigh line such as (-4, -2) (0,0) (2,1) this is straight line passing from the origin. Intuitevely pca2 would be 0 as I have not up ...
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Dimensionality Reduction of Curved Structural Data

I have been using PCA dimensionality reduction on datasets that are quite linear and now I am tasked with the same on datasets that are largely curved in space. Imagine a noisy sine wave for ...
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Difference between scaling just x or x and y in PCA / principle component regresseion

Before doing principle component regression it is important to scale the data. But which data exactly? Is it enough if I just scale X or do I have to scale the whole data set, containing X and Y (=...
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PCA dimentional reduction

I try to plot three values of a VCF file (QUAL, DP, and rate of phasing) for all the SNPs in the file. I thought that a PCA plot would be a good way to reduce dimensions of the plot, and to compare ...
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Using PCA for Dimensionality Expansion

I was trying to use t-SNE algorithm for dimensionality reduction and I know this was not the primary usage of this algorithm and not recommended. I saw an implementation here. I am not convinced about ...
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PCA in supervised machine learning

I would like to use PCA in supervised ML aiming to generate a binary classification model. The data set I have consists of well validated target variable (labels) concerning the one classification ...
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Is Multicollinearity the reason behind the poor performace of my ANN model for regression?

So I was working on a tribology dataset to predict residual depth.It is a regression problem but when I saw the correlations values between the independent variables they were more than 0.9 so OLS ...
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Interpretation of PCA/FAMD results

I wrote a code about a mix PCA (FAMD - factor analysis of mixed data), where I have a dataset with some categorical variable and some numerical variable. This is my example code in R: ...
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How to plot a PCA table [closed]

I'm studying PCA method with the package PCAmixdata because I have a dataset with numerical and categorical variable. This is my example code in R: ...
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Nearest neighbor face recognition in eigenspace when using dot product of test set with eigenvectors does not match the performance when using sklearn

I am trying to perform Face recognition using PCA (eigenfaces). I have a set of N training images (of dimensions M=wxh), which I have pre-processed into a vertical ...
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PCA analysis graph

I'm trying to draw a graph for a PCA analysis. This is my example R code: ...
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PCA result interpretation

I'm studying PCA and I'm trying to apply this method to mtcars dataset in R. This is my code: ...
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When standardizing data, does that imply that the mean and standard deviation will become 0, respectively 1?

As title suggests, I've been wondering about how standardization works when trying to understand how Principal Component Analysis( PCA) works from this tutorial https://medium.com/analytics-vidhya/...
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How to define the features that bring more variance?

I have a dataset with 10 column, that are my features, and 1732 row that are my registrations. This registration are divided in 15 classes, so I have several registration for every class in my dataset....
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Reconstruct low dimensional PCA projection

Suppose I have high dimensional data (say 32 dimension) and I projected the data onto 2D space. How can I project (or approximate) these 2 dimensional point back onto the original 32 dimensions. \ In ...
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Different types of Component Analysis on subsets of data

I have a dataset with 25 columns, of which 10 are continuous quantitative variables and 15 are either binary or ordinal categorical. I have two question: Can I apply PCA only on the continuous ...
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How to compute constant c for PCA features before SGDClassifier as advised in Scikit documentation?

In the documentation for SGDClassifier here, it is stated; If you apply SGD to features extracted using PCA we found that it is often wise to scale the feature values by some constant c such that the ...
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SKLearn PCA explained_variance_ration cumsum gives array of 1

I have a problem with PCA. I read that PCA needs clean numeric values. I started my analysis with a dataset called trainDf with shape ...
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How to reduce columns with binary data

I have one-hot encoded some categorical columns and as you might have guessed the resulting data frame is very large and I'm looking for a way to reduce the dimensions of this data frame. PCA is not ...
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PCA in Tensorflow

I am currently trying to understand the code of PCA in TensorFlow Extended. I tried to understand how the Covariance Matrix is calculated once we have a PCollection (Beam). I wanted to execute the ...
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Which clustering model to use when doing market segmentation with survey data

We are looking to run a survey to determine the needs of our customers. For a needs-based segmentation model, can I run a variety of max-diff, multiple-choice, likert scale questions? Also, what type ...
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Projection of PCA into new variable space (study area)

I have a set of >30 variables distributed in x,y coordinates across two study areas. I'd like to reduce that dimensionality with a PCA applied in the first study area. Then I'd like to visualize ...
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How to use principal components in R to apply a multinomial logistic regression? [closed]

Context: I have an original dataset of +- 20k rows (samples) and 253 features (deletions, insertions and substitutions). These columns/dimensions/features are called SNVs (single nucleotide variants ...
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How to interpret data projected on the sum of first few principal components weighted by eigen values?

I have simulation time series data of a molecule from Molecular dynamics and I want to visualize the very high-dimensional trajectory in two dimensions and also identify some clusters. The problem is ...
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PCA vs t-SNE in asset pricing

So I am trying dimensionality reduction techniques on the S&P500 FY2020 data. I understand the CAPM model and the fact that doing a PCA determines my market variability factor (the first PCA ...
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Label PCA "clusters" with unique values from a dataframe in a graph

Context: I'd like to label each "cluster" accordingly to some labels. I have about 20k rows with > 50 columns (dimensions) and one of the columns are the labels (for a total of 19 levels ...
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Factor Analysis vs PCA

Could someone please explain when FA is used or when PCA is used, as I understood FA do dimensionality reduction, however PCA - the main goal is the same. Then which one should I use and in which ...
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Can PCA help to reduce false positives in image-based classification?

I'm working on a 2-class problem where cancer cells need to be accurately identified from a mixed population containing cancer cells + white blood cells (WBCs). The model I have been using - SVM with ...
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FactoMineR: detecting dimensions with noises

I have some categorical datasets and was considering to do MCA using FactoMineR. Is there a way to detect dimensions with noises in FactoMineR? Considering to do a cluster analysis afterwards so it ...
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Combining two MCA before clustering

I have two kinds of data with several categories and multiple variables. I have done a MCA analysis on both of them to reduce the dimension. Now, would like to make a cluster analysis combining both ...
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How to retrieve flatten PCA analysed image?

I am trying to retrieve each of the images after PCA. What I have done, First, I flatten all the images and concatenate them vertically. Suppose, image size is 80 x 80, then flattening it creates 1 x ...
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How to obtain vector representation of phrases using the embedding layer and do PCA with it

I am trying to understand from both a conceptual and a Python code point of view, how to represent phrases that are present in a corpus (that is used to train a neural network to classify phrases) as ...
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What are the differences among Proper Orthogonal Decomposition (POD), Singular value decomposition (SVD) and principal component analysis (PCA)?

Proper Orthogonal Decomposition (POD), singular value decomposition (SVD), and principal component analysis (PCA) are three eigenvalue methods used to reduce a high-dimensional data set into fewer ...
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How to implement the contribution analysis using PCA?

I have been looking into implementing the Q-Residual and Hotelling's T statistics calculation to the PCA components which is similar to the following article and website: Structural Health Monitoring ...
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Analyzing Microscopic Residue

My goal is to analyze a set microscopic residue. I'd like to apply entropy discretization to attribute A1, segmentation by natural partitioning to A2, then correlate A1 and A3. The first attribute of ...
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1 answer
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how to classify highly overlapping data after PCA and t-SNE?

I'm working on a classification (3 classes) of unbalanced weather data having 22 features. Even after applying PCA and t-SNE the data is overlapping. The best classification score achieved so far is ...
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How to multiply a pandas dataframe with a numpy array

I have a simple program for performing PCA analysis on a dataset. The goal is to multiply the dataset by the feature vector at the end of the program. ...
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2 answers
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Does PCA helps to include all the variables even if there is high collinearity among variables?

I have a dataset that has high collinearity among variables. When I created the linear regression model, I could not include more than five variables ( I eliminated the feature whenever VIF>5). But ...
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How to perform pca analysis with pandas

Here is my dataset: ...
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Practical Interpretation of PCAs for a supplier analysis

I am using PCA to validate and research a set of 13 suppliers of products against a set of about 50 variables and performance indicators against an ideal "wish"-Supplier, mostly based on G. ...
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Trying to understand a PCA output

I recently ran a code to generate PCA for a movie ratings dataset. Actually there were two different datasets, a 'movies' and a 'ratings' one. The movie had about 9700 rows of different movie titles ...
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What is the best way to flatten my data to be used by an SVM algorithm?

I am trying to classify data from an 8 channel SEMG sensor (different gestures) by using an SVM. So far, I have managed to record the data and for each channel, I've calculated 7 appropriate features, ...
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Running PCA on top of tf-idf features?

Is it a good idea to run PCA on top of attributes obtained with Tf-Idf? The tf-idf returns a lot of attributes so in that case I believe it is a good idea to run PCA to reduce the number of dimensions....
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How do you know PCA would work on your dataset?

From my understanding, PCA assumes that redundancy in features can be explained by linear relationships. It also finds orthogonal bases, so when the variance of your data is maximized along non-...
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How is CBOW different from building PMI matrix and then reducing using PCA

PMI matrix and reduction using PCA: Based on the number of times 2 words appear together (in a certain pre-defined window), and the individual frequency of words, we build the PMI matrix. Then reduce ...
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Can I use PCA to create new songs?

So lets create a vector space where the magnitude is the pitch of a note and each note in a song is a new orthogonal vector. Would I be able use principal component analysis to describe enough of the ...
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2 votes
1 answer
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Why does classifier (XGBoost) “after PCA” runtime increase compared to “before PCA”

The short version: I am trying to compare different classifiers for a certain dataset from kaggle, and am trying to also compare these classifiers between before using PCA (form sklearn) to after ...
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