Questions tagged [pca]

Principal component analysis, a technique for dimensionality reduction.

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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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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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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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Does the Orange PCA plugin deal with Categorical Values?

On my dataset, I have several categorical variables (with more than 2 categories). Does Orange can deal with them ? I found some Python and R solutions here and here but I would like to know if any ...
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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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about SVD and PCA [closed]

i want to know when should i use SVD and when should i use PCA or the different purpose of each ? also if i run SVD on a matrix and i made a scatter plot for the svd$d and i found for example the ...
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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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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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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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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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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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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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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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Suggestion of a model for these type of data?

I've got a data set that looks like this ...
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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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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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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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Principal component analysis

I have a data set that looks like the following: ...
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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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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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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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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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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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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 ...
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Can we use pca for supervise classification?

My questions are: Can we use "pca feature selection" for supervised classification? What will happen to labels when we use dimension reduction? If I understand it right when we use pca for feature ...
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1answer
63 views

Feature importance after PCA (or other dimensionality reduction methods)

I have text data which I one hot encoded and then used PCA on it (although I'm experimenting with other methods as well, LDA, NMF..). I am using the result of the dimensionality reduction as an input ...
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Plotting two PCA using same Principal Components to compare data

Background: I have been given a task to replicate functionalities of an old data analytics tool on Python. This tool has set of examples, one of which has a data of a chemical process end of which, ...
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1answer
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How to find which features have been selected by PCA algorithm?

I used PCA function in MATLAB to decrease features on my data set. By this code I can reduce features from 12 to 8(as an example). It works good but my question is that how can I found with feature ...
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1answer
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Does PCA decrease the feature on my Data set or just decrease the dimension?

I'm new in AI and sorry if my question is simple. I have a data set and want to use PCA to decrease the feature but after some research on the internet I'm confused about decreasing dimensions and ...
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Measuring distance preservation in dimensionality reduction

I am looking to compare the distance preserved during dimension reductions for several techniques. I have read some papers on similar topics here and here. For example, I would like to use the ...
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Model for time series analysis

I'm new to data analysis and ML in general. I'm working with some friends on this problem: We're trying to predict when a component of a machine will stop working properly so the client can change it ...
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Is it OK to try to find the best PCA k parameter as we do with other hyperparameters?

Principal Component Analysis (PCA) is used to reduce n-dimensional data to k-dimensional data to speed things up in machine learning. After PCA is applied, one can check how much of the variance of ...
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Convert categorical data in numeric preserve euclidean distance

I m looking how to preserve Euclidean distance with categorical attribute. Ad example, if I have a dataset with attribute of people, Age, weight etc..and i find a attribute "sex" where contain "...
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Having difficult interpreting the eigenvectors for a simple 3x2 matrix

I calculated the eigenvectors and eigenvalues from a covariance matrix given a data matrix of 3 columns and 2 rows. I am trying to interpret results but I can't understand on how to interpret them. ...
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1answer
28 views

Can you apply PCA to part of your dataset?

I am working with kaggle dataset that has over 130 features composed of 116 categorical and 14 continuous features. I plotted the heatmap for the 14 continuous variables and found that most of them ...
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81 views

Scaling sparse data for PCA

Not sure how I should interpret the scaling. Is it correct to convert the sparse matrix to a dense matrix by padding with 0's and scale normally?
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Best classification technique for following kind of data set

I have a large table where each record or row represents a single salesperson, and there are 50 columns or dimensions where each column represents one of 50 products potentially sold by any given ...