Questions tagged [pca]

Principal component analysis, a technique for dimensionality reduction.

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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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1answer
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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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24 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
21 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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Suggestion of a model for these type of data?

I've got a data set that looks like this ...
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1answer
48 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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1answer
25 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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24 views

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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1answer
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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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23 views

Principal component analysis

I have a data set that looks like the following: ...
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2answers
26 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
27 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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47 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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1answer
75 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
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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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1answer
21 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 ...
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1answer
35 views

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
33 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
30 views

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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1answer
73 views

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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106 views

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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1answer
34 views

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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1answer
34 views

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
23 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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1answer
49 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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60 views

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 ...
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165 views

Why do we choose principal components based on maximum variance explained?

I've seen many people choose # of principal components for PCA based on maximum variance explained. So my question is do we always have to choose principal components based on maximum variance ...
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1answer
39 views

target in cluster analisys (PCA)

i m doing dimensionaly reduction using PCA. I don't understand why some dataset already had a target ad example in Iris database or other like this (https://scikit-learn.org/stable/datasets/index.html)...
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2answers
39 views

In PCA, every principal component a eigen vector?

In pca, we convert predictors into principal components for dimensionality reduction. My assumption is every principal component is a eigen vector with eigen value as sum of squared distance of ...
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1answer
38 views

PCA in visual Analytics

I m studying visual analytics and i have a theoretical question about this topic. My professor introduced this schema in him slide For connect data to visualisation. Some topic is very easy to ...
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4answers
49 views

Is dimension reduction helpful to select features for a classification problem?

Let's say I have a data set but I don't know what features are relevant to solve a classification/regression problem. In this case, is it worth/good to use a dimension reduction algorithm and then ...
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1answer
308 views

PCA, SMOTE and cross validation- how to combine them together?

I was reading a lot recently about PCA and cross validation and it seems that the majority call it malpractice to do PCA before cross validation. I would also like to perform SMOTE, but there is a ...
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1answer
46 views

Many things behave differently in high dimensional space

It turns out that many things behave very differently in high dimensional space. The below paragraph is picked from a book. I need extra help to understand. The book says, if you pick a random ...
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20 views

How to compare Factor and Principal Component Analysis results?

I am currently working on an assignment where I am to perform a comparison of different Dimension Reduction techniques in Python. I am using the Scikit-learn functions to perform PCA and FA. However I ...
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2answers
38 views

Similarity measure before and after dimensionality reduction or clustering

I have a dataset with 500 000 samples, each sample contains 30 features. The values of the features are in the range 0.0 to 1.0. ...
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1answer
39 views

Scale of the data after PCA

I have 4 standard normal features on which I perform PCA. I then take the first principal component (with all of the components). Is it possible to a priori say what is the max and the min value that ...
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1answer
38 views

How can we apply PCA to reduce dimensionality of a heterogenous dataset?

I have a dataset containing insurance Claims with quantitative and qualitative variables but PCA refuses to convert or work with "string" type variables. This is the code I used : ...
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PCA on conditional heteroscedastic timeseries

What is the correct method of application of PCA on time series data. Since the time series may exhibit conditional heteroscedasticity, application of normal PCA might be wrong as the variance changes ...
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1answer
14 views

How the original data can be written in the space defined by these M principal components?

Suppose you apply PCA on the data $x_1,...,x_6$ and find that data can be fully described using M principal components $u_1,...,u_M$. How the original data can be written in the space defined by these ...
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1answer
26 views

Given a 12x12 binary image (only black and white pixels) what is its dimensionality? And how can I define dimensionality of a data space?

Suppose I have a grid 12x12 of pixels that can be only black or white. I can't understand if the dimensionality is 2 or 3. I mean... Is dimension given by 12x12 or 12x12x2 ?
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1answer
29 views

Ploting eigenvectors

I've generated two clouds of 3d points from multivariate_normal ...