Questions tagged [pca]

Principal component analysis, a technique for dimensionality reduction.

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21 views

Principle Component Analysis on multiple functions

I am working on a research project that uses data of geometrical structure complexities of different land coverage types (primarily cities, pastures and natural structures). My supervisor has ...
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How can I normalize my output data for neural network?

I have a dataset consisting of 5 numerical variables, like the sample in the following image: In this dataset the first four variables are the input and the GDP is the output. I am trying to build a ...
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27 views

Do I use the mean vector from my training set to center my testing set when dimension reducing for classification?

Please let me know if this is the right place to ask this (or if any of my tags are wrong) or if I need to write this any differently. Do I use the mean vector from my training set to center my ...
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29 views

Clustering after PCA: Use the standardized data, or take into account the variation explained at each PC?

I am interested in clustering daily gridded data. Because of the many dimensions (gridpoints), I first perform PCA to reduce the dimensionality and keep the n-first PCs that account for at least 85% ...
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35 views

can I use t-sne or PCA to reduce number of classes?

I wanted to know if I can use t-sne or PCA to reduce the number of classes depending on the similarity between them. For example, if I have 100 classes of 100 different animals and would like to put ...
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Original Features Identification After PCA Analysis

I have performed a PCA analysis over my original dataset and from the compressed dataset transformed by the PCA I have also selected the number of PC I want to keep. Now I am struggling with the ...
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1answer
31 views

implementing an algorithm that mixes data clustering and linear regression

i have the following dataframe available in the link as a csv, it conveys information about stars. more specifically - column ID represents arbitrary ID of sample. column z represents my target ...
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1answer
34 views

How to comptute principal component from three points in two dimensional space?

I have the following question: Given 3 points (-1, 1), (0, 0), (1, 1). What's the first principal component and what are the coordinates of the projected data points? What would be the variance of ...
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33 views

What is the best way to store images in python for machine learning

I am currently working on a classification problem that requires me to classify whether an image contains cancerous tissue cells or not. Each image is 50x50x3 pixels, the 3 is for RGB values. So far ...
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Potential speedup by applying PCA once on dataset with m rows vs. IncrementalPCA to x batches of size m/x?

I've been working on trying to perform dimensionality reduction on high-dimensional, high-volume datasets (with many rows and columns - around 100,000 - 1M rows and ~500 columns). While the size of ...
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(Incremental) PCA for all data

I'm using PCA to find prime components that are covering most of the variance in my dataset. What I usually do is I run a PCA for all components, then see how many components cover most of the ...
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Analogy between Autoencoder and PCA

I know that Autoencoders can be regarded as non-linear generalisations of PCA, but I struggle to understand in depth the analogy between the two. Once PCA has been performed on a function $F(\vec{\...
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How do I interpret my result of clustering?

I am working on a clustering problem. I have 11 features. My complete data frame has 70-80% zeros. The data had outliers that I capped at 0.5 and 0.95 percentile. However, I tried k-means (python) on ...
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1answer
33 views

PCA Regression Problem

I have a regression problem whereby my data has 21 features and I wish to apply dimensionality reduction using PCA. As far as I know, all the tutorials I have seen so far use PCA for classification ...
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44 views

comparison of t-SNE and PCA and truncate SVD

How to compare the trucate SVD ,PCA, and T-SNE? What we can say about features if t-SNE and PCA and truncate SVD digaram is in this figure?
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1answer
29 views

reduction of model accuracy while using PCA for a regression problem

I am trying to build a prection problem to predict the fare of flights. My data set has several catergorical variables like class,hour,day of week, day of month, month of year etc. I am using multiple ...
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41 views

Intuitive explanation of difference between PCA and SVD [closed]

Can someone explain the difference between SVD and PCA with real life example?
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PCA before Affinity Propagation (AP)

I have a large-ish dataset (100k samples, ~100 features), that I am trying to cluster, to an unknown number of clusters. I thought of using PCA first, to reduce dimensionality, since I understand that ...
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105 views

How do I perform a Kaiser-Meyer-Olkin test (KMO) on Orange?

How do I perform a Kaiser-Meyer-Olkin test (KMO) in Orange? I am trying to reproduce, in Orange, some processing done in SPSS. There I used the syntax below to run a factorial analysis: ...
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PCA, why variance of eigen values is measure of its utility?

Source - Murphy, 12.3 Heuristic for assessing applicability of PCA. Let the empirical covariance matrix Σ have eigenvalues λ1≥λ2≥···≥λd>0, with mean λ. Explain why the variance of the eigen values, ...
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34 views

PCA and orange software

I am analysing if 15 books can be grouped according to 6 variables (of the 15 books, 2 are written by an author, 6 by an other one, and 7 by an other one). I counted the number of occurrences of the ...
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45 views

How many dimensions should matrix be reduced to with PCA?

I want to reduce the dimensions of my dataset to decrease the complexity without losing too much information. But I am not sure how many dimensions I should reduce to, so that I don't lose too much ...
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92 views

How to decide the number of primary components for PCA

Background Trying to identify the number of primary components to use (k) for PCA for MNIST aiming at 95%. ...
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34 views

Samples in a PCA form lines, what are the reasons?

I have come across some PCA plots in which the samples are forming "lines", such as this one: Or this one: What kind of data can generate such PCA plots? Are there anything wrong with these PCA?
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Differences and similarities between nonnegative PCA and nonnegative matrix factorization

I have seen references in the literature to nonnegative principal component analysis (nPCA) and nonnegative matrix factorization (NMF). I have tried reading the papers on both of them but it is not ...
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193 views

PCA scikit-learn - ValueError: array must not contain infs or NaNs

I use PCA from from sklearn.decomposition to reduce data dimension. ...
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1answer
29 views

Dealing with irrelevant features in dataset (Homework)

I have a specific question pertaining to one of my machine learning homeworks. Basically, we are required to build a model that takes a 5000*10000 dataset X (5000 examples each with 10000 features), ...
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104 views

Intuition behind PCA eigenvectors

For undergraduate students who understand the definition of eigenvectors and eigenvalues, $$A v = \lambda v \;,$$ what is the intuition behind why the eigenvectors of the covariance (or correlation) ...
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Error in the train function

I'm building a model and i want to re preocess the data using PCA but i have an error in the train function : ...
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Can PCA reduce dimensionality of a subset of data whilst still considering the whole set?

I have a nominal set of known data (some 400x400 matrix) and a much larger additional set (~400x40000). Adding each additional column of data-values from the larger set will increase the practical ...
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3answers
36 views

Aggregate several attributes into one using PCA

I am working on a soccer dataset. As we all know, soccer has a lot of different metrics related to the game. We have Key passes, Accurate Passes, Shots on Target, Saves, Clearances and so on... I have ...
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Suggestions on non-linear dimensionality reduction for small, one-hot encoded dataset

I wish to apply non-linear dimensionality reduction on a very small dataset (less than 100 observations). The dataset is very sparse, of approx 20 columns, each containing either 0 or 1. It's the ...
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1answer
152 views

'PCA' object has no attribute 'explained_variance_'

Elbow Method - Finding the number of components required to preserve maximum variance. My code: ...
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0answers
105 views

How to plot k-means scatter plot on Standardize Data for 16 features in python? Is it even possible?

I have 16 features in my dataset:['age','job', 'marital', 'education', 'default','balance','housing','loan','contact','day','month','duration','campaign','pdays','previous','poutcome']. And result as :...
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1answer
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Interpreting the new dimensions after PCA

I have telecom data with large number of dimentions. Now if I apply dimentionality reduction like PCA then from resulting dimention say PC1, PC2 I would loose the meaning or would not understand what ...
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3answers
92 views

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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1answer
83 views

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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1answer
95 views

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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1answer
52 views

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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2answers
43 views

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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25 views

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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1answer
24 views

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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1answer
322 views

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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1answer
44 views

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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273 views

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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1answer
124 views

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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1answer
28 views

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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44 views

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
219 views

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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