Questions tagged [pca]

Principal component analysis, a technique for dimensionality reduction.

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PCA & Pre processing

Do I have to apply PCA after preprocessing the dataset or prior to preprocessing? After preprocessing (encoding) the data the number of columns is increasing to over 100 so should I be reducing the ...
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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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135 views

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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Perform PCA on images

I'm actually studying how to perform a PCA-based image compression. In Hernandez & Mendez's Application of Principal Component Analysis to Image Compression, an approach to compute the Principal ...
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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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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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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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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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Theoretical differences between KPCA and t-SNE?

I (think I) understand the underlying principles of most dimensionality reduction methods (MDS, IsoMap, t-SNE, Spectral Embedding, Diffusion maps, etc...). Some of the algorithms I use the most are ...
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25 views

Would it be a good idea to use PCA output as input in models?

I have some dummy variables that indicate the occurrence of an event. There is so many of them, so I used PCA on them, and it appears some of them are rather correlated together. Would it be a good ...
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135 views

Variable importance and correlation PC's - variables

Can I relate features importance output of RF and the correlation's values between principal components and features? After RF, I used clustering (useful for my purpose). To find the features that ...
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781 views

Is it always possible to get well-defined clusters from the data?

I have TV watching data and I have been trying to cluster it to get different sets of watchers. My dataset consists of 64 features (such as total watching time, percent of ads skipped, movies vs. ...
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What can we learn from PCA on non linear data?

Suppose we have dataset with 10 features which are not linear: ...
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T-SNE with high number of features

If we have high number of features (more than 50), should we use T-SNE ? According to https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html: It is highly recommended to use ...
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Normalization before PCA in NLP domains?

I'm working on a basic bag-of-words toy NLP pipeline for sentiment analysis using scikit-learn. From research of other questions here, it seems that the main applicable scaler for before PCA is the ...
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How to run PCA when data contains some categorial features?

Assume that we have a dataset with various features, and some of the features are categorial. And PCA dosn't work good on categorical features. How should I handle such datasets using PCA, what is ...
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What we can learn from the data if PCA scree plot bins are almost the same?

Suppose we have a data-set with 4 features. Suppose we calculate the PCA for this dataset and we plot the scree-plot: What we can learn from the features? Can we ...
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PCA in a 3D torch

I have a 3D tensor. The rows are different dialogues. The columns utterances (1,...,n) from the dialogue and the cell the ...
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ValueError: Input contains NaN, infinity or a value too large for dtype('float32'). Any advice? [closed]

The error below presented itself when attempting to assemble a PCA. My code: ...
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Difference between PCA and regularisation

Currently, I am confusing about PCA and regularisation. I wonder what is the difference between PCA and regularisation: particularly lasso (L1) regression? Seems both of them can do the feature ...
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XGBoost:Logistic poor performance with Scaled and PCA data

Working on a data set similar to fraud but not monetary transactions. Here are the steps that I have taken on the modeling side: Convert Some of the categorical into numerical (One hot encode) Over ...
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Reducing features to 2 by PCA confusion Matlab

I am trying to reduce $500$ features to $2$ as an assignment. I wrote the following code and I am deeply concerned if it is true as when I plot it on the graph it does not look good. It should look ...
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confusion about PCA algorithm matlab

I am given a data set with 500 features and I am told to apply PCA and find most important 2 features. I wrote some MATLAB code but I am very confused with all this PCA thing. ...
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Profiles classification

I've been working on a project where it consists of a dataset of profiles (50k rows) along with their position, age group and hobbies (200 columns). These features (except for the position) are graded ...
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189 views

Can someone clarify what the linear assumption of PCA is?

0 For the past few hours I've been trying to search what this linear assumption is. Some of the articles states that that your independent variables have to be linear in relationship and need some ...
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Identifying subset of feature set to represent remaining features using pca or any other techniques

How can you use PCA to identify a subset of feature set to represent remaining features in a data set? Suppose there are 10 features given by F={ f1,f2......f10}. How to identify a subset of F such ...
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61 views

Computing SVD through Eigendecomposition of correlation matrix

I am following the excellent series on SVD by Steve Brunton from the University of Washington, on YouTube, but I have trouble interpreting his 4th video on the subject. If I understand correctly, he ...
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Is it possible to do Robust PCA on Images

I have a task to perform outlier detection on a medical image data set (CheXpert), but with more „classical“ techniques (i.e. not use a DL approach like GANs or AEs) and I was wondering whether robust ...
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Understanding clusters after applying PCA then K-means

I have a dataset grouped by customer level, and the rows are sum_mexico, sum_uk, ... etc to indicate if the customer has spent money at stores in those countries..similarily counts for these as well. ...
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Showing a graph with PCA selected features in Random Forest Model

So I created a Random Forest Model like so: ...
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185 views

Eigenvalues of covariance matrix are negative

I'm working on the PCA of the mnist dataset, and I get a very strange result, I created a matrix whose rows are flattened mnist images, When I try to compute the eigenvalues of the covariance matrix, ...
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24 views

How to interpret the original time series approximated using principal component analysis? [closed]

I've read some posts about PCA applied on time series, but still a bit confused and I have the following questions(Suppose I am working with a time series of the return of 50 industries and I want to ...
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Is there a way to implement nested features in unsupervised models?

Our project has built an unsupervised model that uses data about a number of companies. Some of these companies are public and some are private. The ones that are public have much higher financial ...
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Difficulty understanding the dimension differences in kernel PCA

In Kernel PCA, the kernel trick works because we can show that there is an equivalency between eigenvectors of the kernel matrix and eigenvectors of the covariance matrix. I know the math to go from ...
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Calculation of PCA

Consider the following data set : Now we need to calculate the principal component analysis for this data. Here are the eigenvalues and eigenvectors calculated for the covariance matrix of this data :...
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Considerations to take into account when clustering

My idea is to use clustering to perform stock segmentation based on risk, building different risk levels that might adapt better to different kind of users. Hence I have computed different risk ...
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52 views

Is there any different between feature selection and pca? If there is could anyone please kindly explain for me please?

First of sorry for asking a possibly beginner question, but i don't understand pca seems to be the same as feature selection, but when i search online they seems to be talked differently. What people ...
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81 views

Perform PCA on columns of different length

I have about 20-30 columns, all with different lengths. The first column has 25000 rows, the second column has 19000 rows, and it's different for all the columns. All are the survey data with 0 (No),1(...

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