# Questions tagged [pca]

Principal component analysis (PCA) is a statistical technique for dimension reduction often used in clustering or factor analysis. Given any number of explanatory or causal variables, PCA ranks the variables by their ability to explain greatest variation in the data. It is this property that allows PCA to be used for dimension reduction, i.e. to identify the most important variables from amongst a large set possible influences.

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### Images dimension reduction

I have a 3000 of images each of which has shape of (200,180,3).
I want to reduce their dimension.
Therefore, I use PCA, I want to do it firstly without using any library.
The first problem I camed ...

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25 views

### PCA - Plotting individual distance to principal component

I am doing a PCA analysis with the CRAN iris dataset. I wonder how I can create the following plot:
I want to select the first principal component and for this component I want to plot the distance ...

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25 views

### Diffrent PCA plots

I was trying to to learn pca(using the iris dataset) with python and i got some results,so i wanted to test the results ir R to make sure it was good.When i checked the results,it gave me a mirror ...

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25 views

### Fuzzy c-mean clustering and evaluation methods

I am trying to use Fuzzy c-mean clustering over my data. I would like to show only cluster n = 2. I have tried this code and it works but I am having a problem if I modified to print only cluster 2.
...

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29 views

### How can I compute T2 Hotelling after PCA?

I need to compute Hotelling T2 and SPE (Q), after the PCA analisys. I did it using the pca function from library mdatools, but I see the PC computed are different from the one computed by prcomp or ...

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35 views

### Principal Component Analysis Code in Python

I need a help to code PCA in python
I've tried some code but the result in python is different from the result in Minitab

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12 views

### n-components doesn't seem to truncate the number of components calculated

I'm trying to perform Kernal Principal Component Analysis (KPCA) on a large data set that I will want to find the pre-image of after removal of the low energy/high entropy components.
I would had ...

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31 views

### scikit-learn PCA for image dataset

I am trying to perform PCA on an image dataset with 100.000 images each of size 224x224x3.
I was hoping to project the images into a space of dimension 1000 (or somewhere around that).
I am doing ...

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15 views

### How can I extract the decoder part from an autoencoder, using syntax similar as in my example?

I built an autoencoder, and I'm trying to extract the decoding part so I can visualize 'eigenfaces', by giving the hidden layer (or the input layer in the decoder) input that assigns 1 to a single ...

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26 views

### prcomp “Error in colMeans(x, na.rm = TRUE) : 'x' must be numeric”

I'm brand new to R and I'm presently trying to create a PCA plot for a project. I created tables of my data in excel and then saved it as a .csv file, which I declared as a variable as follows:
> ...

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38 views

### noise reduction using python regarding other people's sound as noise

I want to use python to dispose of an Audio file which can recognize only my voice. For example, I speak to a raspberry pi car about "forward". It will go straight but other people who speak "forward" ...

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### How to generate scatter plot from the vector values in each row for PCA?

I have successfully created label and feature vectors and am able to apply pca analysis on it but what happens is that the column generated is of datatype vector and each row is a vector. How do I ...

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35 views

### Useless SVM model, is my data useless or am i using libsvm wrong?

I am trying to use the CBIS-DDSM dataset to classify malignant or benign breast tumours with PCA and SVM.
However, my results are astonishingly bad, and I have been working my head of the last week, ...

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19 views

### problem of misalignment in Houghlines detection with python

[Hough Lines detected in red][1]
HI, i'm working on an app and i need to extract these bands by using hough but i have some problems while extracting them like misalignment of the detected lines.
Do ...

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29 views

### ValueError: Wrong number of items passed 6, placement implies 11, i dont get this

I tried to reduce the dimensionality of a data frame with PCA, but when I run my program it shows two errors, do pandas have inner attributes?? how can I fix this??
ds = pd.read_csv('forestfires.csv')...

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21 views

### Positioning Multivariate Data into 2-dimensional Space (with PCA)

I have multidimensional data. (11 columns - attributes , 150K rows - number of data). It is slightly sparse-alike data, for example, which means one datum has numeric values like (0, 0, 6.5, 0, 0, 7.5,...

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### Why do I get “weka.attributeSelection.PrincipalComponents: No attributes!” while running WEKA PCA in Java?

I have to make a visualization of a 4 dimension dataset (plus the class attribute). To this purpose, I want to run PCA on the whole dataset. Since I don't have to do any machine learning on the ...

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16 views

### How to get a low dimensional rank of non-negative factorization matrix

I have a big matrix
X = numpy.random.rand(1000, 1000)
using sklearn.decomposition I factorized the matrix such as:
from sklearn.decomposition import NMF
model = NMF(n_components=1, init='random', ...

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23 views

### Truncated SVD is taking lot of time

I'm trying to reduce dimension of data set by computing what can be the best n_components using truncated SVD but its taking lot of time.
from sklearn.decomposition import TruncatedSVD
pca = ...

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40 views

### principal components of PCA

I came across this question in datacamp.com:
Bellow are three scatter plots of the same point cloud. Each scatter plot shows a different set of axes (in red). In which of the plots could the axes ...

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25 views

### scikit learn PCA - transform results

I have a timeseries of first differences onto which i apply PCA using scikit to get the first PC
# data is a timeseries of first differences
pca = PCA(n_components=1)
pca.fit(data)
pc1_trans = pca....

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38 views

### Principal component analysis in matlab?

I have a training set with the size of (size(X_Training)=122 x 125937).
122 is the number of features
and 125937 is the sample size.
From my little understanding, PCA is useful when you want to ...

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27 views

### Biplots for Functional Principal Component Scores

I'm trying to get the biplots between two F. principal components (or harmonics). I provide an example from fda package doc. to solve the riddle:
library(fda)
#BASIS FUNCTIONS
daybasis65 <- create....

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13 views

### How to obtain the output graph from the dataset

May I know how to modify my Python programming thus I will be able to obtain the same result as refer to the image file
import numpy as np
import pandas as pd
df_wine = pd.read_csv('https://archive....

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33 views

### How to select a subset of a dataframe using a variable dynamically

I have an R dataframe with 300 columns.
I have done Principal Component Analysis and grabbed the top 110 columns that explain the variability of dataset.
How do we pass the 110 column names list to an ...

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32 views

### What do the differences mean between pyspark SVD Eigenvectors vs. PCA Eigenvectors?

I'm using the SVD and PCA functions in (pyspark) mllib (Spark 2.2.0) as described in this link: https://spark.apache.org/docs/2.2.0/mllib-dimensionality-reduction.html
Suppose we are given the ...

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32 views

### How to use principle components selected by PCR in further SVR analysis?

I want to use principle component regression to find essential components and then extract those components to apply further SVR analysis, but I got some problems when doing this.
First try, I follow ...

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33 views

### Applying PCA to one sample

I am currently working on an image recognition project with machine learning.
The train set has 1600 images with size 300x300, so 90000 features per image.
To speed up training, I apply PCA with ...

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37 views

### Applying PCA to a covariance matrix

I am have some difficulty understanding some steps in a procedure. They take coordinate data, find the covariance matrix, apply PCA, then extract the standard deviation from the square root of each ...

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59 views

### How to calculate covariance matrix of data frame

I have read data frame of sensor data, using pandas read_fwf function.
I need to find covariance matrix of read 928991 x 8 matrix. Eventually,
I want to find eigen vectors and eigen values, using ...

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46 views

### What is the meaning of 'components' in principal component regression?

I'm learning principal component regression and I don't understand the result I get from PCR method. My goal of using PCR is to reduce the number of predictors.
For example:
library(caret)
# Load ...

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6 views

### Prediction on test data using logistic regression coming negative and more than 1 also

Logistic regression model prediction on test data is negative as well as more than one. Whereas probabilities range from [0,1].
I have scaled data (both train and test ) using standard scaler and ...

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44 views

### How to use pca function in MATLAB to select effective features? [duplicate]

I'm new in pca and after some researching I found that with pca algorithm we can select best effective features.
I just wanted to use pca function (in MATLAB) to select best features to ...

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27 views

### How to deal with memory error while using make_meshgrid()

I am trying to visualise SVM classification results using Matplotlib and Scikit-learn, how to handle MemoryError ?!
For my example, I have a small dataset, a table X of 100 examples and 10 features (...

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### when I execute a KernalPCA I get a LinAlgError

when I execute the KernalPCA code on Kaggle predict House Prices, it returns me such errorSVD did not converge in Linear Least Squares
from sklearn.decomposition import KernelPCA
from sklearn....

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### glm.pcr throws error referencing matrix/vector

PostDF <- read.csv("https://raw.githubusercontent.com/thistleknot/Capstone-577/master/output/V7221-greaterEqual-10-filtered.csv", header=TRUE, sep=",")[,-1,drop=FALSE]
x <- PostDF[,-1, drop=...

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23 views

### The relation of eigenvalue and PCs in PCA [migrated]

In R, I got the result of PCA and eigenvalues and vectors
and three eigenvalues above 1 were checked.
If so, is it valid data from PCA results to PC1 ~ 3?
Here is my eigen values and vectors,
eigen(...

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38 views

### PCA for KNN: preprocess parameter in caret

I am conducting knn regression on my data, and would like to:
a) cross-validate through repeatedcv to find an optimal k;
b) when building knn model, using PCA at 90% level threshold to reduce ...

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30 views

### How to reduce a 1D word2vec vector's dimensionality with PCA?

Suppose I have the following word embedding vector:
vec = np.array([1,2,3,4,5,6,7])
What is the correct way of reducing the dimensionality of this vector from a 7 dimensional vector to a 2 ...

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21 views

### What is explanation behind the linear combination in PCA?

I have used a Principal Component Analysis on a panel dataset in R, since I'm new to both, I am unable to understand why the summation of each principal component across variables is not 1 or if it ...

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29 views

### To derive Principal component in functional PCA

I am trying to get the way to find eigenfunction from eigenvalues. As far as I understand from reading, I have to multiply eigenvalues by something to get principal components in functional PCA. But ...

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17 views

### How to define dimensions in fviz_cluster with PAM data?

I have a data frame which is divded as samples in rows and variables in columns
Upon doing a PCA:
df.pca <- PCA(df, graph = FALSE, ncp = Inf)
df.coord <- data.frame(df.pca$ind$coord)
...

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53 views

### Negative eigenvalues in PCA

I've a matrix x (1000*25) that contains random floats in the interval (-5,5). nFeatures=25 and nPoints=1000. I'm using this code to find the eigenvalues of the covariance matrix, but I'm getting ...

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### Number of principal components in SVM parameter tuning vs. final evaluation

I am using PCA to reduce the dimensions of my data (50 samples x 32767 features) before feeding it to an SVM. I am using the following cross-validation scheme for tuning parameters of the SVM kernel, ...

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42 views

### Relationship between input variables and principal components in PCA

Here is the result of PCA.
RC1 and RC3 can be interpreted which variables are related.
But, can not interpreted in RC2.
When the eigen value is checked, the number of factor is 3.
But can there really ...

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44 views

### Applying PCA on a specific column of a pandas Dataframe

I'm trying to reduce the number of features of a dataset of images so that cosine similarity computes faster.
I have a pandas dataframe that has the following structure ["url", "cluster_id", "...

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54 views

### How to plot a circle for each point scatter plot while each has particular radius size

I have a pandas frame with distance matrix, I use PCA to do the dim reduction. The the dataframe of this distance matrix has label for each point, and size.
How can I make each scattered point ...

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33 views

### After using kmeans(): how to determine which point belongs to which group?

I am running a kmeans clustering to identify labeled data. I ran pca and then kmeans and got the following plot using ggbiplot:
Now, how can I determine which point belongs to which group in table ...

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32 views

### PCA preprocess parameter in caret's train function

I am conducting knn regression on my data, and would like to:
a) cross-validate through repeatedcv to find an optimal k;
b) when building knn model, using PCA at 90% level threshold to reduce ...

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**0**answers

16 views

### PCA or Linear Discriminant Analysis ? Classification Problem in QoS

I'm working on a classification problem related to the marking of ip/tcp packet, the classes are Best Effort and Non Best Effort; I'm using Python language. I have selected these features: Protocol, ...