Questions tagged [pca]

Principal component analysis, a technique for dimensionality reduction.

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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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24 views

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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1answer
15 views

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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32 views

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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1answer
57 views

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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10 views

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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1answer
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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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15 views

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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1answer
59 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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14 views

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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1answer
56 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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1answer
25 views

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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1answer
60 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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1answer
22 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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14 views

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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47 views

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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33 views

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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32 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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1answer
56 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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121 views

Dot product and linear regression

I'm studying PCA and my professor said something about finding the linear regression by doing the dot product of both axis. Could someone explain to me why? The dot product returns a number. What's ...
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46 views

whether to use normalization while performing PCA?

I have an excel file containing a table where I registered the frequency of three linguistic phenomena in 72 poems. Since the poems have different lengths I normalized the results dividing each value ...
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15 views

How to do feature reduction for a log-linear regression model

I'm building a log-linear regression model and I have 18 different variables in my model. 13 out of 18 variables I'm using are hot-encoded variables for holiday, e.g. showing which holiday it is. I ...
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How to check PCA in Orange?

Input file: T X Y A 1 3 A 5 8 B 7 6 B 4 2 T is categorial target variable I calculate PCA in Orange: components X Y 1 PC1 -0.707107 -0.707107 2 PC2 0.707107 -0.707107 T PC1 PC2 1 A 1.58032 -...
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Help why to apply PCA here

Lets say we have a dataset of 9 dimensional points and I want to apply k-means algorithm. I was studying an example where they apply PCA before fitting the data into the clustering algorithm. The ...
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22 views

Order of eigenvalues when using different methods

I'm doing PCA in a covariance matrix where each column and row represents tenors of the yield curve. I have coded the Jacobi rotation method and I also have a QR algorithm based on numpy.linalg.qr in ...
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1answer
82 views

PCA, covariance, eigenvector matrix and rotation [closed]

I am following the Coursera NLP specialization, and in particular the lab "Another explanation about PCA" in Course 1 Week 3. From the lab, I recovered the following code. It creates 2 ...
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18 views

How many pictures are needed to generate eigenfaces that correctly represent the input faces?

I would like to show how eigenfaces work. Therefore, I would like to take 5 pictures of the person who is using my code and then after PCA reduction I would like to show the eigenfaces which represent ...
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I need help in PCA results using WEKA Tool [closed]

I'm working on an experiment using KDD'99 cupset I have 42 features. the paper I 'm comparing with concludes that 3 features with precision ..% ok are the best subset to identify the attack X. In my ...
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21 views

SVD based method for predicting missing values in future experiments

I would like to pose my question in terms of a hypothetical example, where I have a matrix: 11 12 13...1m 21 22 23 ..2m . . . 3m . . . . . 11 n2 n3 . nm The matrix has no missing values. ...
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Do i need sort the data by the eigenvalues with descend (PCA)

after computing the eigenvectors and eigenvalues. the eigenvectors sort by eigenvalues on descending. data * eigenvectors = transformed data how about the standardized raw data, do I need to sort them ...
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2answers
64 views

Principal components analysis need standardization or normalization?

Principal components analysis need standardization or normalization? After some google, I get confused. pca need the scalar be same. So which should I use. Which technique needs to do before PCA? Does ...
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1answer
35 views

PCA ? after the transformed data, are they still same with original data, (if maintain same dimensional) [closed]

after the step of pca, I try to plot them (see pic) example in the original (A) 2 features => pca 2 features if I do not reduce any dimensional data, are they still have the same meaning of these ...
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21 views

With NN Model data preprocessing, is One Hot Encoding WITH PCA a good or bad idea?

With Tabular Data (containing both continuous and categorical data) preprocessing, does the One Hot Encoding of Categorical Features help or hinder the effectiveness of PCA prior to Neural Network ...
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1answer
48 views

Using PCA as features for production

I struggle with figuring out how to proceed with taking PCA into production in order to test my Models with unknown samples. I'm using both an One-Hot-Encoding an an TF-IDF in order to classify my ...
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16 views

Dimensionality Reduction for Function Fitting method using Kernels

I have a set of continuous noisy measurements $x_i \in R^n$ with $i=1,...,N$ for which I know the value range, i.e. $x_{min} \leq x_i \leq x_{max}$. Corresponding to the measurements, I have a set of ...
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1answer
62 views

What are the model parameters in PCA? [closed]

I've been asked to report the number of parameters to be learned in a PCA model. This answer implies that parameters do exist in PCA, but does not explain. Software packages often report the number of ...
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2answers
36 views

How to perform Anomaly Detection on a force profile?

I have a set of force profiles of an industrial machine. I'm trying to develop an algorithm that tries to understand when a new profile is "anomalous" with respect to the ones in "...
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1answer
56 views

I want to run PCA on a data set that will be aggregated by country. Should I aggregate the data before or after I standardize the data, and why?

Basically the title asks my question. I have the results of a survey that was filled out by people from different countries. I have been asked to analyze the data using PCA and see what findings I can ...
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1answer
95 views

Why are eigenvectors produced by np.linalg.eig different than the PCA components stored in the instance of the PCA object?

I am trying to understand why eVec (produced by np.linalg.eig) is different than ...
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1answer
193 views

How do I determine which variables contribute to the 1st PC in PCA?

Given the coefficients of PC1 as follows for each variable (0.30, 0.31, 0.42, 0.37, 0.13, -0.43, 0.29, -0.42, -0.11) which variables contributes most to this PC? Does the sign(+/-) matters or ...
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1answer
76 views

Ordering of standardization, pca, and/or tfidf for neural network

I have 60k rows of text data. I have tokenized it into 55k columns. I am using a neural network to classify the data but have some questions about how to order my preprocessing steps. I have too much ...
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0answers
25 views

Dealing with high dimensionality datasets

I have data of dimensionality (25000, 100, 500) i.e. 25000 rows each consisting of a 2 dimensional 100 X 500 matrix. Currently I am only applying CNN for ...
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24 views

need to create 4 group using the data

i have to create four group using this data :- ...
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1answer
20 views

Clusterize Spectrum

I have pandas table which contains data about different observations, each one was measured in different wavlength. These observsations are different than each other in the treatment they have gotten. ...

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