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Questions tagged [pca]

Principal component analysis, a technique for dimensionality reduction.

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20 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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46 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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3answers
109 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
25 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
32 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
26 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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1answer
72 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
41 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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0answers
13 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
29 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
27 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
30 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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0answers
20 views

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
13 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
15 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
28 views

Ploting eigenvectors

I've generated two clouds of 3d points from multivariate_normal ...
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1answer
33 views

How to mathematically explain the translational and rotational invariance of PCA

There is a homework question for a course I am self studying (not a student) that is: let our $n \times d$-dimensional data vectors be denoted by $x_1,\ldots,x_n$ and let $R$ be a $d \times d$ ...
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0answers
53 views

t-SNE on extremely high-dimensional spaces

I successfully applied t-SNE to the number handwriting dataset. n=3823 data points (i.e. handwritten numbers) in an D=64 dimensional space (i.e. 8x8 pixels). Worked great. Now I would like to cluster ...
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0answers
15 views

I need help interpreting this PCA plot

I have a dataset of 116 observations and 10 numeric variables. The dataset contains information about healthy patients and patients attained with breast cancer. I did a PCA plot showing the cluster of ...
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0answers
48 views

PCA for unsupervised feature selection [closed]

If I understood correctly, "using results of PCA to select features" (as recommended in this answer) implies visually analysing bi-plots of first two principal components - i.e. the angle between a ...
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0answers
20 views

Show importance of variables from a data set without a response variable? Use PCA? [closed]

I am trying to find a way to statistically show that some variables in my data set are more important than others to determine its classification. I have an example data set with three variables from ...
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0answers
23 views

I have data of some movies and their subtitles.I want to classify them based on their ratings

I will convert the subtitles into vectors and use them as features to classify the movies into different categories based on their ratings.The problem that I am facing is my feature vector is much ...
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0answers
29 views

PCA based anomaly detection

Let there be a data in the form of a ellipse where the minor axis corresponds to a normal data samples and major axis has an outlier, such that outlier has maximum variance from mean. In this case, ...
2
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1answer
57 views

PCA: projection of positive data on negative side of plane

I did PCA on my data and projected the data on first two eigen vectors. After projection I see that the scatter plot of the data starts from [-1,-1]. My data is all positive. Is it correct for the ...
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1answer
54 views

Guidance needed with dimension reduction for clustering - some numerical, lots of categorical data

I've my data in a Pandas df with 25.000 rows and 1.500 columns without any NaNs. Of the columns about 30 contain numerical data which I standardized with StandardScaler(). The rest are cols with ...
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2answers
142 views

multivariate clustering, dimensionality reduction and data scalling for regression

I have a dataset with approximately 20000 observations consisting of 40 independent and 1 dependent variable. My initial objective is to develop a model that will predict the dependent variable. I ...
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3answers
30 views

What best/correct algorithm/procedure to cluster a dataset with a lot 0's?

I'm new to statistics so sorry any major lack of knowledge in the topic, just doing a project for graduation. I'm trying to cluster a Health dataset containing Diseases(3456) and Symptoms(25) ...
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1answer
54 views

Is PCA (by eigendecomposition) or SVD better in decorrelating the predictors of a machine learning model?

Is there any reason to think that SVD is better than PCA (by eigendecomposition) in decorrelating the predictors of a machine learning model?
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1answer
30 views

How to choose PCA or KernelPCA a priori?

I am learning about dimensionality reduction and I understood that one of the most used techniques in ML is PCA. If I understood correctly, I use PCA whenever I want to reduce the number of features ...
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0answers
39 views

PCA and FastICA in scikit-learn giving near identical results

So after importing my data, transforming it, and splitting into training and test sets I tried running this script for PCA: ...
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2answers
55 views

Differences between applying KMeans over PCA and applying PCA over KMeans

Short question: As stated in the title, I'm interested in the differences between applying KMeans over PCA-ed vectors and applying PCA over KMean-ed vectors. Long question: Let's suppose we have a ...
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2answers
453 views

sklearn.decomposition.PCA explained_variance_ratio_ attribute does not exist

When trying to identify the variance explained by the first two columns of my dataset using the explained_variance_ratio_ attribute of ...
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1answer
16 views

What does it mean if a high or low number of my componenets describe a percentage of the cumulative explained variance?

In the following code run after PCA i can see that X number of components explain Y % of cumulative explained variance (CEV). I would like to know 1- What percentage of the CEV is typically ...
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1answer
37 views

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
54 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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2answers
44 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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2answers
54 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
118 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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1answer
123 views

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
276 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
199 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
84 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
59 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
37 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
176 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
2k 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
507 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 ...
2
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1answer
145 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
162 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
129 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 ...