Questions tagged [pca]
Principal component analysis, a technique for dimensionality reduction.
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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:
$$
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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 ...
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Cluster-Based Anomaly Detection (and PCA)
I have a dataset of user operations (250 types of operations) in a trading platform and my task is to extract features, flagging rules, other insight for fraud prevention/anomaly detection, or some ...
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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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Question about PCA cross validation & categorical data
I have been reading the book Hands of ML of Geron and a question arose for me the PCA you do after Normalization.
let's say u do a cross validation u do multiple PCA's, there could be a bug in some ...
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Encoding of categorical variables to reduce the effect of erroneous labels
I have a structured dataset containing (nominal) categorical variables encoded as labels, let's say a feature includes labels from 1 to 20. Some of the labels in that feature could just be errors, ...
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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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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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How to choose the optimal PCA kernel
In a chemometrics application, I need to reduce the dimensionality of a spectral scan. The standard PCA is linear. Not sure if the data is. How do I choose the most optimal PCA kernel?
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What is the functioning of Rotation Forest when replacing PCA with Random Subset Feature Selection (RSFS)?
So guys, i've been lately reading about Rotation Forest algorithm and i learned that it uses PCA in order to reduce the feature space and it also uses a decision tree as base classifier. The authors ...
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how to use numpy mutual information correctly
i want to use principal component analysis-mutual information (PCA-MI) to have data representation from source which has source relevance of (value from smartinsole) and ouput variable (value from ...
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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 is PCA applied in CV with multiple channels?
I am currently working with a CNN with a physical magnitude dataset. The goal is to downscaling one of them based on the others, but previously I would like to do dimensionality reduction.
If I have ...
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Dimensionality Reduction of Categorical Variables in Spark
First off, I hope this question hasn't been asked already. I've found questions regarding the use of PCA vs. MCA in the reduction of categorical variables, but I've yet to see a solution as it ...
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What Feature Scaling to use before a PCA on image features extracted by a CNN?
I'm developing a general purpose reverse image search, I came up with this pipeline:
feature extraction using a pre-trained CNN (pretrained on ImageNet), reducing the dimensionality using ...
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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 ...
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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(\...
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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 ...
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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).
<...
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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:
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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 ...
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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 ...
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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
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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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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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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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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 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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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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