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Questions tagged [pca]

Principal component analysis, a technique for dimensionality reduction.

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Can anyone explain me the difference between Factor Anaysis and PCA?

Is PCA and Factor Analysis same? Both are used for Data dimension reduction but theoretically I am not able to find the difference between them? I did FA in SPSS to reduce number of variables in my ...
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0answers
23 views

A classification machine learning flow chart implimenting dimentionality reduction, upsampling, and cross validation [closed]

I would like to make a flow chart for an ML classifier and make sure that my thinking is correct. Here is a little about my sample: I have 3 classes and about 160 features. I suspect that some of ...
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11 views

Can I use MCA on categorical features, and PCA on numeric then combine both for learning

So all is said in the title. I have a mix of both categorical and numeric features, both are more than 20 columns and reside in the same data-set. I am using PCA solution from sklearn.decomposition ...
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2answers
32 views

How to plot High Dimensional supervised K-means on a 2D plot chart

I'm Having a ML problem where my data set contains 80 features labelled into 3 groups (0, 1, -1). I want to plot the data on a 2D surface to see how "close" (similar) data with ...
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31 views

Does PCA need unevenly distributed data to be successful/meaningful?

I am not quite sure about these assumptions here, but if my data is evenly distributed in the feature space does that mean that PCA will have no impact on the classification result? My thought ...
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2answers
39 views

Is deduction, genetic programming, PCA, or clustering machine learning according to Tom Mitchells definition?

Tom M. Mitchell defines machine learning as A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, ...
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3answers
103 views

Are dimensionality reduction techniques useful in deep learning

I have been working on machine learning and noticed that most of the time, dimensionality reduction techniques like PCA and t-SNE...
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25 views

Feature selection after performing PCA

I have a data set with 57 variables on which I am performing PCA. The PCA returns me a list of principal components, each of which is in turn a list of the loadings to be placed on my variables in a ...
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0answers
41 views

How to use SVD to reduce the dimension of test data which is not available at the time of SVD?

Usually, when both train and test data are available in the beginning, a dimensionality reduction such as Singular Value Decomposition (SVD) can be applied on both of them as one matrix. The reduced ...
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24 views

Number of clusters with eigengap method in spectral clustering

I would like to find the number of clusters for spectral clustering which I could apply to my data. I've tried eigengap technique on a cosine similarity matrix and got the following plot: The gap ...
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19 views
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Reconstructing original data points from t-SNE output

I have been trying to understand t-SNE for a while now and I have this very basic question on the comparison of PCA and t-SNE, on which I would really appreciate some help. In case of PCA suppose the ...
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2answers
226 views

Distributed PCA or an equivalent

We normally have fairly large datasets to model on, just to give you an idea: over 1M features (sparse, average population of features is around 12%); over 60M rows. A lot of modeling algorithms ...
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1answer
56 views

How to use multiple encoders(one-hot and numerical) together for PCA

I want to implement PCA on a dataset(retail) but the data is categorical. One-hot encoding on some columns like Gender, Fabric, Brand makes sense but on other features like price range, size, I would ...
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1answer
20 views

PCA - Error minimization and Variance Maximization

I'm studying the PCA algorithm and the theory behind it. I think I understood how does it work and the idea of dimension reduction of the data in order to find a new feature (component) that maximizes ...
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2answers
36 views

How can we interpret biplot?

This is not a question as such but more likely to be verification (enhancement) of my current understanding. With the thought that it may help future visitors as well, I am taking liberty to make this ...
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2answers
32 views

Does high regression coefficient for Principal components that don't explain much variance imply that my data is not a good predictor?

There isn't much to add to the question. Essentially i had some data that I reduced to 4 principal components, the first two components of which explain 99% of the variance in my data. Upon building ...
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1answer
34 views

Using PCA to cluster multidimensional data (RFM variables)

So i am performing k-means clustering on RFM variables (Recency, Frequency, Monetary). The RFM variables are in the form of quantiles (1-4). I used PCA and found the PCA components. I then used the ...
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3answers
217 views

Predictor Variable vs. Target Variable [closed]

If I find that a variable is not a predictor variable, does that mean it automatically becomes a target variable? I don't believe so because if you have 209 variables, and you find your predictor ...
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1answer
128 views

Data scaling before PCA: how to deal with categorical values?

I have to apply PCA on a dataset, which contains both numerical and categorical values. In the preprocessing phase, I converted all the categorical values in numerical, so that the software can deal ...
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1answer
43 views

How can I apply PCA to KNN?

I want to know the do not want to how to use library I will denote a $n\times p$ data matrix by $X$, where $n<p$. That is, each row of $X$ is one sample data with $p$ feature variables. By using ...
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1answer
45 views

Sklearn Pipelines - How to carry over PCA?

I'm developing a pipeline to fit parameters for a gradient boosting classifier while also fitting the optimum number of features in a PCA model. This is the current setup: ...
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1answer
84 views

Sklearn PCA with zero components example

I'm simply trying to repeat a benchmark from the sklearn's docs. The unclear part is: n_components = np.arange(0, n_features, 5). They are applying a PCA transform ...
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0answers
19 views

PCA huge parts of missing data filling

I’m performing PCA on different time series’ and then using K Means clustering to try and group together common factors. The issue I’m facing is that some of the factors come in and out of the time ...
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How to read factor vs. factor plots?

How to read factor vs. factor plots? Particularly, what do negative values mean? Also, why is it possible for features to have negative effect on the factors?
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2answers
89 views

deepAR RNN from AWS Sagemaker - should I clean the data first?

I test the Sagemaker AWS solution for RNN: deepAR. Previously I used sklearn for this and obviously I cleaned the data to avoid highly correlated time series (KBest,...
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38 views

Which algorithm to use when data set has a lot of categorical variable levels and a few numerical variables?

I'm working on a data set containing some numerical and some categorical values. Though after some research I got to know that I can go for encoding techniques to convert categorical to numerical. But,...
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1answer
63 views

Principal Component Analysis and abnormal data

I know that PCA is good in differentiating between anomalies and normal data and it helps to differentiate between them when it tries to transfer the data to another dimension. I mean it can somehow ...
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496 views

ValueError: operands could not be broadcast together with shapes (60002,39) (38,) during pca.transform

I am trying to solve the San Francisco Crime Problem on Kaggle. To begin with, here is my code: ...
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309 views

How to implement PCA color augmentation as discussed in AlexNet

I read through "ImageNet Classification with Deep Convolutional Neural Networks" again specifically for details on how to implement PCA color augmentation. I am unsure if I have it right. Here is how ...
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3answers
80 views

Whats an explanation of PCA that is intuitive for someone in senior leadership who doesn't have a technical background?

I've been pulled onto my first Data Science project at work. Classic problem of predicting sales based on web traffic data, etc. While I don't know about the specific techniques I will be using in my ...
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1answer
35 views

very low variance explained after applying pca

I applied PCA on MNIST data and found that the first 64 components are able to retain 86% of variance. Is there any problem while applying pca to a big dataset like MNIST. Because in most of the ...
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33 views

Scripting inverse PCA or MNF in Orange (biolab)

I have a data table with many rows and columns that I have successfully taken through K-means and PCA workflows in Orange. Now I'm trying to calculate the "minimum noise fraction" of the data. This is ...
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1answer
40 views

Should Principal components be normalized before applying K means on them?

I want to get the Principal components of a dataset and apply K mean clustering on them. Do I need to Normalized the PCA output before applying Kmeans on them ?
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43 views

Performace of Fischer projection as dimension reduction compared to other LDA methods

How is the performance of Fischer projection compared to other LDA methods of dimension reduction? I thought that Fischer projection was a great method of dimension reduction by maximizing class ...
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40 views

Replace NaN values in data for pca and using measured errors in linear regression

I have a matrix with 11 variables and 1258 observations for each variable, and another matrix the same size with the corresponding measurement error for the variable. In two variables i have a NaN ...
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2answers
37 views

distinguish users for recommender system

How can you calculate better video recommendations for a SmartTV app which is used by multiple users in a household. I don't know which user is currently watching the video because the account for the ...
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2answers
67 views

How to use pca results for linear regression

I have a data set of 11 variables with allot of observations for each one. I want to make linear regression on the variables with the observed $\vec{y}=\alpha +\beta*\vec{X}$ when X is matrix. I'm ...
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1answer
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PCA or cluster table of experimental fitness scores

I need to find patterns experimental data. The columns are "experiments" which are chemical treatments for growth experiments. The rows are individual gene names, the values are a fitness-defect ...
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22 views

Optimal dimensionality reduction methods for large square, mirrored, matrices (MxM)?

I have 10,000 x 10,000 (up to 100,000 x 100,000) matrices. The matrices denote pairwise similarity between elements of a same sequence. M(1,2) is the similarity between points 1 and 2. M(500,4250) is ...
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54 views

PCA with Catagorical Variable in R

Is there any Package for PCA for data having Categorical variable ?
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1answer
61 views

Including the dependent variable in your data to perform principal component analysis?

Let's say you have a data set with GPA (dependent variable) and Amount of alcohol, Amount of study, IQ, and SAT score as the independent variables. And you want to perform the principal component ...
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1answer
143 views

Under what conditions should an autoencoder be chosen over kernel PCA?

I've recently been looking at autoencoders and kernel PCA for unsupervised feature extraction. Lets consider just for a moment linear PCA. Its my understanding that if a autoencoder (with a single ...
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1answer
58 views

PCA first dimension do not not capture enough variance

I am doing a PCA as a data exploration step and I realize that the two first principal components capture only 25% of the variance, the ten first principle component capture about 60% of the ...
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1answer
214 views

Can PCA be applied to reduce dimensionality of only a subset of features?

Lets say I have a feature set of f0 to f1000. I am thinking of applying PCA on f500 to f1000 reducing their dimensionality. Can ...
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71 views

Different results with the same flows

I've tried to repeat the same results with the same flow, and I don't understand the results are different in each situation. I describe the situation I have a file with 192 instances and 37 ...
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1answer
65 views

Dataset of extremely low-dimensional images for PCA

I am looking for a public data-set of images that differ from each other only slightly, so that after applying PCA they can be reconstructed with a small error from ...
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4answers
1k views

Is PCA considered a machine learning algorithm

I've understood that principal component analysis is a dimensionality reduction technique i.e. given 10 input features, it will produce a smaller number of independent features that are orthogonal and ...
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2answers
152 views

Does it makes sense to combine PCA with an artificial neural network?

I have a Dataset of around 200 features. Most of them are categorical and only a few are numerical. It seems that an artificial neural network with an Autoencoder has some problems with that kind and ...
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2answers
121 views

Should I apply PCA on the entire dataset or just the nominal values?

I have a data-set with 14~ attributes, roughly half of them nominal. I've used a binary vectorizer to convert these values to a number of attributes. The number of attributes, naturally, ballooned up; ...