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Questions tagged [pca]

Principal component analysis, a technique for dimensionality reduction.

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Standardize (rescale) after PCA to run a regression?

I standardized my panel data, applied PCA and extracted two principal components. Now I want to use them to run a fixed effects regression. I have quite a few interactions (double and triple) between ...
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Dimenson reduction from a cosine similarity matrix

I have a silly little question: I have 200 press articles (string), I vectorize these articles with an embedding model (sentence embedding), so I have 1024 values per article. I then have a 200 x 1024 ...
Bertrand's user avatar
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Treat Age as Categorical or Continuous

I am in the middle of processing data for feature reduction (haven't decided the method yet). The data consists of a row of people and ~3000 column of attributes that correspond to that person and is ...
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Can principal components changed by a normaliaation method be used to construct original data shape with SVD

I would like to use an algorithm called Harmony to normalize my data. Harmony takes as input principal components ($PC$), and ...
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Finding parameters which reveal clustering in t-SNE

These data are from SAMHSA, Mental Health Client-Level Data. I am trying to find the right parameters to obtain clustering as in this paper. Code here. For now, I'm dropping columns which aren't ...
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Ways to check whether your predictors are even correlated with their label?

I scraped a dataset of pre-match data in a video game and am trying to classify them by outcome, i.e. wins v. losses. The models I've tried so far have poor accuracy of around 50%, so I'm thinking ...
Lilian Shi's user avatar
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How can I calculate the data energy loss after PCA?

Recently in a slide in about PCA (Principal Component Analysis) I saw a question: "How much is the data energy loss in PCA?&...
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How to reduce number of continuous variables before I make a set of best predictors (for handgrip strength in women)

My assignment question is quoted: "2. Which set of variables best predicts handgrip strength in women? a. Reduce the number of continuous variables before doing the analysis." I do not ...
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Assessing performance of a generative model (images) using Jensen-Shannon Divergence

Background I am currently working on comparing two high-dimensional image datasets (MNIST) of shape $\sim(50000, 256)$ to evaluate the performance of a generative model, and have attempted to use ...
Andrea's user avatar
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How to explain the new features after a PCA?

Let's say I made a PCA in which I reduced from 10 dimensions to 3. And it clusters the classes correctly, but how do I explain which dimensions are better to predict? It is obvious that the 3 ...
MAD MAGGOT's user avatar
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PCA for correlated variables

I have a question about PCA. I know that if you have correlated variables (x1, x2, x3, x4), its good to go do the PCA so that you can have new uncorrelated variables (pc1, pc2) used instead of the ...
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When visualizing graph nodes, should I use apply PCA to node2vec embedding?

I am trying to visualize graph nodes using node2vec embedding. The node2vec embeddings has lengths of 50~100 dimensions. I have two plans: use umap to project node2vec embeddings to 2D space use PCA ...
Sijie Chen's user avatar
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Problem with 2 variable PCA loadings- Loadings are the same for all variables

I'm working on a problem for which i want to do some dimensionality reduction using 3 different PCAs of 2 variables each. Basically i want to perform a PCA and keep the first component between the ...
Felipe Maresca's user avatar
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252 views

How is PCA applied to (one-hot encoded) DNA sequence data?

I realize some questions have been asked already about one-hot encoding for PCA. The answer seems to be along the lines of 'The PCA will run, but does not necessarily make sense.' However, I have a ...
Chris_abc's user avatar
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Dimension reduction of Word Embeddings: PCA vs. TSNE

I am pretty new to DS. I have a general question regarding the limitations of visualizing word embeddings using PCA. I've learned so far that when using PCA (e.g. with ...
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How to evaluate-visualize clusters derived through PCA

the title might not be the best to adress my question. Here is my problem I have a data set with 21 features. and I want to cluster the data to interpret if there are any insights that I can have by ...
Mehmet Deniz's user avatar
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important feature selection using dimensionality reduction algorithms

I have a dataset having more than 25000 features. I did perform noise removal using the histogram approach, and this dataset gets reduced to more than 5000 features. There are two classes, healthy and ...
Gajanan Kothawade's user avatar
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Deep Learning book - PCA - showing the covariance of z is diagonal

In the Deep Learning book at Page 146, the authors are showing that the covariance of the representation $z = W^T x$ is diagonal (Eq. 5.92 through Eq. 5.95). The terms are conveniently arranged: $$ ...
borijang's user avatar
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best algorithms for clustering customers, customer segmentation

I have a dataset mixture of categorical and numerical variable, I was wonder what are the best algorithms to cluster customers? how to find the underlying patterns that segments a customer??
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What is the difference between SMOTE before PCA and after PCA

We all know that PCA (Principal Component Analysis) is a popular statistical tool to reduce the dimensionality in a dataset. SMOTE (Synthetic Minority Over-sampling Technique) allows you to generate ...
Sivadithiyan official's user avatar
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Principal component analysis ('function' object is not subscriptable)

I am trying to use a principal component analysis to determine the most important features in my dataset. When I run the code below, I receive an error saying "PCA object is not subscribable.&...
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Looking at feature contribution after classifying groups using components

I have a lot of features and many are correlated, so I performed dimensionality reduction. I then used these components in binary classification and got high accuracy. I also performed feature ...
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Implementation of PCA algorithm for reconstruction of data(images)

I'm learning the theory and implementation of PCA algorithm in the book 'Mathematics For Machine Learning' and finishing the official tutorial notebook in ...
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2 votes
1 answer
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Identifying competition climbing styles from World Cup data

In competitive climbing, it is well known that there are many different styles, differentiated by e.g. the steepness of the climbing wall. Some athletes excel at certain styles more than others. I ...
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Is covariance matrix required to compute SVD?

In the PCA lecture of Andrew Ng's ML in Coursera, it is computing the covariance matrix $\Sigma$ from X and use it to compute SVD. Is covariance matrix of X ...
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Variable selection and NA

I have a very large dataset with a lot of NAs in the data. I want to perform an analysis and have to select the variables that are of most interest. I feel like I have to take 3 steps before I can ...
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Implementing eigen decomposition

Question Please help understand why the eigen vectors do not match below. If there are misunderstandings or incorrect place, please correct too. It would be much appreciated. Eigen decomposition ...
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How to use new data with Principal Component Analysis (PCA)

I reduce data with PCA already from 9 to 3 feature. If I have real data new row which I want to use with pre-train model (.h5). Can I change data 9 feature to PCA 3 feature only one row for test with ...
user572575's user avatar
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How can PCA be distributed among workers?

I want to distribute the work from PCA among a set of workers. So let samples $x_1,... , x_d\in \mathbb{R}^n$ be samples. Then to find a dimension reduction subspace we need covariance matrix $cov(\...
A.Dumas's user avatar
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Finding the most statistically significant variable(s) in a dataset - logistic regression/feature selection

I'm currently working on a project where I have a dataset which consists of a number of blood samples and the quantity of different biological compounds within each sample. The samples are split into ...
Vintagefiretruk's user avatar
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120 views

PCA and Variance of 50% only

I have just followed this tutorial in order to try to understand PCA. https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60 However I used a different dataset (Water potability). <...
Luis Valencia's user avatar
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Classification problem with no context in numerical features

I have an extremely abstract and numeric data with equally abstract objective. I have around 3000 rows of train data (df_train), where I have a binary target ...
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PCA followed by UMAP then go into Random Forest

Is it a valid procedure to apply PCA to your dataset and then apply UMAP clustering on the PCA data, before sending the embedded cluster data to a Random Forest classifier? Summary of process: ...
Joe's user avatar
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I need help with which features to use for clustering

I am using this dataset: https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents and so far I have successfully cleaned the dataset as well as reduced the size of the features and records. I have ...
Giannhs Meh's user avatar
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Is TF-IDF for text classification transferable between corpuses?

I am using TF-IDF for text classification and my solution works well according to the performance metric of my choice (F1 macro). To speed up the training process I have used PCA to reduce the ...
Ali Asgari's user avatar
1 vote
1 answer
92 views

Principal Component Analysis (PCA) for non-continuous numerical data

I am learning PCA and the question is the following: can be PCA applied to a dataset containing both numerical continuous and numerical discrete variables? Thank you
BlueSea's user avatar
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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 ...
Peter's user avatar
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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 (...
aryan's user avatar
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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 ...
Bryon's user avatar
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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 ...
Bryon's user avatar
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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 (=...
Sally's user avatar
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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 ...
Drosera_capensis's user avatar
1 vote
2 answers
214 views

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 ...
yeyosef's user avatar
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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 ...
Shashank Singh's user avatar
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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 ...
zr0gravity7's user avatar
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1 answer
122 views

PCA result interpretation

I'm studying PCA and I'm trying to apply this method to mtcars dataset in R. This is my code: ...
Inuraghe's user avatar
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1 vote
1 answer
36 views

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/...
Neri-kun's user avatar
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1 answer
61 views

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....
Inuraghe's user avatar
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1 answer
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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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