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Questions tagged [pca]

Principal component analysis, a technique for dimensionality reduction.

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PCA and orange software

I am analysing if 15 books can be grouped according to 6 variables (of the 15 books, 2 are written by an author, 6 by an other one, and 7 by an other one). I counted the number of occurrences of the ...
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24 views

How many dimensions should matrix be reduced to with PCA?

I want to reduce the dimensions of my dataset to decrease the complexity without losing too much information. But I am not sure how many dimensions I should reduce to, so that I don't lose too much ...
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39 views

How to decide the number of primary components for PCA

Background Trying to identify the number of primary components to use (k) for PCA for MNIST aiming at 95%. ...
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30 views

Samples in a PCA form lines, what are the reasons?

I have come across some PCA plots in which the samples are forming "lines", such as this one: Or this one: What kind of data can generate such PCA plots? Are there anything wrong with these PCA?
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Differences and similarities between nonnegative PCA and nonnegative matrix factorization

I have seen references in the literature to nonnegative principal component analysis (nPCA) and nonnegative matrix factorization (NMF). I have tried reading the papers on both of them but it is not ...
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PCA scikit-learn - ValueError: array must not contain infs or NaNs

I use PCA from from sklearn.decomposition to reduce data dimension. ...
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1answer
24 views

Dealing with irrelevant features in dataset (Homework)

I have a specific question pertaining to one of my machine learning homeworks. Basically, we are required to build a model that takes a 5000*10000 dataset X (5000 examples each with 10000 features), ...
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85 views

Intuition behind PCA eigenvectors

For undergraduate students who understand the definition of eigenvectors and eigenvalues, $$A v = \lambda v \;,$$ what is the intuition behind why the eigenvectors of the covariance (or correlation) ...
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Error in the train function

I'm building a model and i want to re preocess the data using PCA but i have an error in the train function : ...
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12 views

Can PCA reduce dimensionality of a subset of data whilst still considering the whole set?

I have a nominal set of known data (some 400x400 matrix) and a much larger additional set (~400x40000). Adding each additional column of data-values from the larger set will increase the practical ...
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32 views

Aggregate several attributes into one using PCA

I am working on a soccer dataset. As we all know, soccer has a lot of different metrics related to the game. We have Key passes, Accurate Passes, Shots on Target, Saves, Clearances and so on... I have ...
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Suggestions on non-linear dimensionality reduction for small, one-hot encoded dataset

I wish to apply non-linear dimensionality reduction on a very small dataset (less than 100 observations). The dataset is very sparse, of approx 20 columns, each containing either 0 or 1. It's the ...
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1answer
28 views

'PCA' object has no attribute 'explained_variance_'

Elbow Method - Finding the number of components required to preserve maximum variance. My code: ...
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39 views

How to plot k-means scatter plot on Standardize Data for 16 features in python? Is it even possible?

I have 16 features in my dataset:['age','job', 'marital', 'education', 'default','balance','housing','loan','contact','day','month','duration','campaign','pdays','previous','poutcome']. And result as :...
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37 views

Interpreting the new dimensions after PCA

I have telecom data with large number of dimentions. Now if I apply dimentionality reduction like PCA then from resulting dimention say PC1, PC2 I would loose the meaning or would not understand what ...
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30 views

How to deal with a feature that has lot of categorical values?

I know this question has been asked before and I have tried a few things but those things are not working as expected for my usecase. I have a 500 length feature vector. One of these features is a ...
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1answer
36 views

Intuition behind the PCA algorithm

I am trying to understand PCA intuitively. Here it goes: After finding the eigenvectors and eigenvalues of the covariance matrix of the dataset, the eigenvalues will represent how spread out the ...
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1answer
59 views

How to reduce dimensionality of 3.2B categorical features?

Background: This means a dataset of 7,000 samples and 3.2B columns, which I would have to read into distributed Spark memory somehow. Obviously I want to reduce the number of columns that gets fed ...
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30 views

How to structure my data into features and targets for PCA on Big Data?

I want to apply the PCA algorithm from Scikit-Learn.(https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html ) At the part where I have to separate the features and the ...
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Interpretation of PCA visualisation

I am trying to build a classifier to predict the ratings of a show during a specific time. I have extracted around 109 features, some relating to the time field namely, Day of Year Month of year ...
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What is the dimension reduction method to large numbers of independent features while only two of them are important?why?

What is the dimension reduction method to model a data with large numbers of independent features (for instance 5k features), while only two of them are important (are effective in cost function)? I ...
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Dimensionality Reduction. How to explain dynamics of feature subset based on all features data?

I have features: f1..f1000. I want to explain dynamics of particular features subset: f1-f5 based on all features data (based on ...
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1answer
22 views

How to use reduced dimensions (of a PCA) for detection purposes?

A general question aiming at the application of a PCA: I want to detect abnormal data points and therefore I want to use a PCA for it at first. The next step is to try several distance functions or ...
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1answer
72 views

How can I adjust the legend when visualizing clusters in two dimensions?

How can I change the legend as we can see now the legend has some cluster numbers missing. How can I adjust the legend so that it can show all the cluster numbers (such as Cluster 1, Cluster 2 etc, no ...
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1answer
23 views

Why is PCA often used before t-sne for problems when the goal is only to reduce dimensionality?

Ex: Matlab's t-sne tutorials frequently use PCA https://www.mathworks.com/help/stats/tsne-settings.html " Process Data Using t-SNE Obtain two-dimensional analogs of the data clusters using t-SNE. ...
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How to use PCA in CNN for image recognition using Keras?

I created a CNN model for image classification and I want to use Principal Component Analysis (PCA) but when I run pca.fit() code, the code still running for hours ...
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67 views

Which algorithm can be used to reduce dimension of multiple time series?

In my dataset, a data point is essentially a Time series of 6 feature over a year per month so in all, it results in 6*12=72 features. I need to find class outliers so I perform dimensionality ...
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Comparison of performance of autoencoder with PCA

I am running PCA and autoencoder (2 hidden layer with relu) on a data. Both PCA and autoencoder give similar accuracy of the order 50%. I have tried different variations of autoencoder: changing ...
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Issue with using PCA on MLPClassifier

I'm trying to tune my MLPClassifier using GridSearchCV, but it takes ages, so I was wondering if using PCA data will decrease ...
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208 views

Kernel PCA and K largest eigenvectors

How can one prove that the optimal kPCA solution $a^*=\{a_1...a_K\}$ are the $k$-largest Eigenvectors of the (centered) kernel matrix $K$? I referred to a lot of resources and couldn't find a proper ...
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33 views

Python sklearn PCA transform function output does not match

I am computing PCA on some data using 10 components and using 3 out of 10 as: ...
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1answer
35 views

Eigen Decomposition of Data Matrix for PCA

In PCA we Eigen decompose the covariance matrix, not data matrix, Is it because most data matrices are non-square. If they were, isn't is correct to eigen decompose data matrix than the covariance ...
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34 views

Suggestion of a model for these type of data?

I've got a data set that looks like this ...
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1answer
56 views

When I should use PCA?

I have a data set with 60000 rows and 32 columns. I want to use SVM (with some more constraints that make it more complicated)and I think 32 columns are too large. So I decided to use PCA. But when I ...
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34 views

Dimensionality reduction based on value of a variable

I have a dataset including 100k high dimensional data (e.g. houses in LA) (dim=100, e.g. house parameters like area, distance to downtown, etc.). Below is the 2-component PCA representation of the ...
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How to export PCA to use in another program

I'm trying to write a random forest classifier for a very large dataset, as such as part of the pre-processing i have applied PCA to reduce from 643 features to 5 PC's. Is it possible to export these ...
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PCA formulation - Deep Learning book by Ian Goodfellow

I am reading this deep learning book by Ian goodfellow. In the PCA formulation in the first chapter i.e Linear Algebra, he mentions the following: we need to choose the encoding matrix D. To do so,...
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76 views

Implication of a dominant Principal Component in PCA analysis

I need help, are there any practical implications of a dominant principal component. For example, if of three PCs, PC1 explains almost 100% of the variance in this dataset, What does this mean in ...
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PCA giving separated results expected (jupyter sklearn)?

I'm a complete newbie to PCA and I have 3 sets of values which I want to plot with PCA. This is what I am using: ...
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How would one reduce dimensionality/covariance in a dataset with nonlinearly covariant variables? Is M-SSA a no-go?

I am familiar with the Principal Component Analysis method of covariance and dimensionality reduction. I am considering using its multivariate time series brother, Multivariate Singular Spectrum ...
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Reducing the dimensions of data who's predominant categorical feature, its layer, has depths that overlaps with other samples layer values

I am working with a data set of soil types with multiple layers of varying depths and sizes with multiple features. There are $1-9$ layers each with differing dimensions, for example, a soil type ...
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31 views

Principal component analysis

I have a data set that looks like the following: ...
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86 views

Standardization After PCA for Kmean clustering

I want to apply Kmean for clustering after PCA dimensionality reduction. I have standardized data with StandardScaler before the PCA, then I want to train Kmeans for finding clusters. However, the ...
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Nice examples/illustrations of CPD/Tucker or other tensor decompositions for a presentation?

I'm giving a presentation on tensor decompositions (especially CPD and TKD) and I'm looking for some nice examples or illustrations to demonstrate usefulness and intuition, most likely on three-way ...
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1answer
56 views

Difference between Factorization Machines and PCA?

Factorization Machines (FMs) are a means to express the high dimensional data into lower dimensions, despite the original data being sparse. How is it different from PCA which itself is a ...
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49 views

K means visualisation after reducing dimensionality with PCA

In clustering ($K$ means, for example) when I have $N$ features and after creating the model (with this $N$ features) to visualize this model I need to reduce this $N$ dimensions into $2$ or $3$ ...
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PCAModel and PipelineModel, how to get explainedVariance

in pyspark PCAModel contains explainedVariance() method , but once you use Pipeline and specify PCA as a "stage", you will get a PipelineModel as an output and this one does not contain ...
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268 views

clustering before or after PCA?

I'm newbie into data science, and I had some problems dealing with my project. I'm trying to visualize multidimensional data into 2D after clustering with using a lot of methods. (kmeans, DBSCAN, ...
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1answer
68 views

Deciding on the number of components in PCA

I have been running my model several times now. Each time i get different results based on what number i put in my PCA component number range (I used raw numbers in the code instead of the range ...
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24 views

Unskewing the Data with the PCA's Help

I'm making some RFM Analyses (Customer Segmentation) and, in order to feed the RFM data to K-Means, I need to unskew the data, as K-Means works best when dealing with symmetrical distributions. One ...