Questions tagged [pca]

Principal component analysis, a technique for dimensionality reduction.

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Data analysis PCA

I have a question about the functioning of PCA. I have a dataset with only 2 categorical attributes out of 9. Is it good to calculate pca between those two? Does it help me understanding anything ...
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7 views

PCA targeted away from some subspace

Is there an existing technique allowing to do PCA maximizing not the variance per se, but the variance away from some direction? Imagine I have high-dim data with two different labels L1, L2 and I ...
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57 views

PCA - what do I do with its results?

I have a data set with more than 20 features, and I applied PCA: ...
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64 views

SGDClassifier and PCA

In here Mentioned: If you apply SGD to features extracted using PCA we found that it is often wise to scale the feature values by some constant c such that the average L2 norm of the training ...
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1answer
35 views

Why don't get the expected result using a SVM training model?

I want to learn a model for recognizing facial emotions. . I used a dataset with 213 samples. I extract firstly features using the Gabor filter. Then, I reduce the data dimensionality with the PCA and ...
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384 views

Training PCA on BERT word embedding: entire training dataset or each document?

I want to reduce the dimensionality of the BERT word embedding to, let's say, 50 dimensions. I am trying with PCA. I will use that for the document classification task. Now for training PCA, should ...
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94 views

Is it possible to apply PCA on different subsets independently?

I need to apply PCA on a rather big set of data, but my machine is not able to handle the workload. So I was considering to split randomly my original set into 4 subsets, apply PCA independently on ...
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35 views

Dimensionality reduction and prediction when all columns have approximately same variance

I have a dataset of 25 columns where the goal is to predict the value of the 25th column based on the previous 24 columns. The dataset is quite big that's why I initially thought to proceed with PCA ...
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1answer
183 views

Not Access to Confusion Matrix in SVM.SVC.score Scikit-learn Python

I used SVM.SVC function to classify. But when I wanted to calculate the weighted and unweighted average accuracy I couldn't access the confusion matrix. Because of svm.SVC.score only provides a ...
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40 views

Standardizing features by one specific feature

I am working on a project with a dataset that looks something like the following: ...
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1answer
24 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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1answer
117 views

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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156 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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1answer
119 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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2answers
96 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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1answer
28 views

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
43 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
271 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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2answers
364 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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240 views

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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43 views

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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196 views

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
58 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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1answer
620 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
113 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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1answer
823 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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92 views

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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232 views

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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126 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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365 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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593 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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55 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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310 views

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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1answer
2k 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
44 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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1answer
167 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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162 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
4k 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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3answers
148 views

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
764 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
323 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
323 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
227 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
68 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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27 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
38 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
2k 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
306 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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