Questions tagged [pca]

Principal component analysis, a technique for dimensionality reduction.

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31 views

First two principal components explain 100% variance of data set with 300 features

I am trying to do some analysis on my data set with PCA so I can effectively cluster it with kmeans. My preprocessed data is tokenized, filtered (stopwords, punctuation, etc.), POS tagged, and ...
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1answer
38 views

PCA & Clustering Confusion

I have a question related to K-Means clustering and PCA. In my project, I have two target classes - 0 and 1- and I am trying to group the records that were predicted as 0 into 5 clusters. I am using ...
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6 views

Factor Analysis with Mixed Data gives many components

I performed Factor Analysis with Mixed Data using PCAmixdata package from R. My dataset consists of 115000 records with 40 features of both categorical and continuous data. I checked the eigenvalues ...
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12 views

Factor Analysis with Mixed Data Concurrent Approach with PCAmixdata in R

I am trying to perform Factor Analysis over Mixed Data using R with PCAmixdata package. My dataset is huge with almost 115000 records and almost 40 features of both categorical and continuous. When I ...
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19 views

Spark: How to run PCA parallelized? Only one thread used

I use pySpark and set my configuration like following: ...
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1answer
17 views

K-means clustering doesn't overlap with scaled dataset but overlaps heavily without scaled data

I'm working in my first data science internship (I'm first year student) and I'm having problems understanding data scaling and PCA. My task is to figure out the best way to classify buildings based ...
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12 views

Why KMeans.score() gives very high value?

I'm working on Udacity's Identify Customer segments project. My problem now is that after applying StandardScaler on the dataset after procedures of cleaning, I used PCA(n_components=36).fit_transform(...
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3answers
250 views

Should I use keras or sklearn for PCA?

Recentl I saw that there is some basic overlapping of functionality between keras and sklearn regarding data preprocessing. So I am a bit confused that whether should I introduce a dependency on ...
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57 views

PCA for complex-valued data

I'm quite shocked for encountering this error on PCA from sklearn ValueError: Complex data not supported After trying to fit ...
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49 views

Clusters: how to improve results for text classification

I am trying to classify texts using kmeans, TfidfVectorizer, PCA. However, it seems that many texts are not correctly classified as you can see: I have texts in cluster2 that should be in Cluster 0 or ...
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22 views

PCA vs.KernelPCA: which one to use for high dimensional data?

I have a dataset which contains a lot of features (>>3). For computational reasons, I would like to apply a dimensionality reduction. At this point I could use different techniques: standard PCA ...
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2answers
35 views

How to determine what algorithm to apply in the following dataset (included)

I have a dataset that I will be using to build a classifier on. Below I have plotted the First and the Second Principal Component of the data using ...
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1answer
23 views

Higher variance in PCA can mean, that data structure is less informative?

I have the possibility to describe data with two different data structures, both data structures are some sort of approximation to the true data. I would like to compare the two data structures with ...
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2answers
17 views

How to tell how much information I lose when I simplify the graph data structure with respect to unsimplified graph?

I have the following problem: I have some sort of data (that I can't publish here, but they are in the form of points with XYZ coordinates) and I can represent them as a collection of graphs i.e. $Q = ...
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1answer
57 views

PCA shows overlapping boundaries, then why SVM performs best

I am trying to understand which model might work for a given problem before trying the models, I find this case against my knowledge. Please guide what I am missing. I am new to Data Science. Here is ...
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1answer
21 views

Should dimensionality reduction be done before k-means clustering if there are many features?

My data contains over 200 features and over 500 observations. I want to place the observations into a number of clusters based on the features that make them different. There are numerous ideas I ...
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1answer
24 views

Feature relevance in PCA + kmeans algorythm

Working on the World Happiness Report dataset, i have N countries with M features and a happiness score. This is the parameter I built 3 classes from: happy, medium, unhappy (numerical intervals of ...
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21 views

Clustering based on missing values

I have a dataframe of the form: ...
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1answer
16 views

How to interpret PCA rankings in Weka

I am struggling to understand what the rankings in Weka are representing. I.e. the coefficients for each attribute in the rank. What is the output in the Weka program for PCA telling me with these ...
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14 views

Same values for PCA Loadings results

I've recently performed a Principle component analysis for my masters thesis where I have 25 network datasets, formatted into graphs and applied 5 measurements to each graph. The measurements were ...
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11 views

Can we use both ridge-lasso and PCA in the same model for better results?

My question here is if we are using the PCA, the dimensionality is reduced and no question of feature selection is required using ridge & lasso. So should I use ride-lasso followed by PCA or I ...
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1answer
22 views

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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12 views

PCA and clustering, regression tree with categorical attributes using R

I am trying to analyze a dataset which has 7 categorical attributes out of 9. Can you please help me? I don't know how to find right instructions to do it. I only learnt how to do it with numeric-only ...
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8 views

Color overlapping in matplotlib, using PCA

I'm plotting 2d data from PCA, and I tried 2 libraries, matplotlib and seaborn, with seaborn I'm having nice overlapping colors, but in matplotlib it's just random, how would I achieve the color ...
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2 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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1answer
46 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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16 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
22 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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58 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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1answer
27 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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9 views

Supervised dimensionality reduction for multilabel data

re there algorithms for supervised dimensionality reduction like Linear Discriminant Analysis (LDA) for multilabel classification? If I understood it right, the implementation of LDA in scikit-learn ...
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1answer
28 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
42 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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1answer
24 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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11 views

Dimension reduction using non-linear PCA

I am working on an undergraduate astronomy research in which we are analyzing geometrical complexities of different sattelite images of man-made and natural structures on Earth. The different images ...
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1answer
22 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
29 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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1answer
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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1answer
40 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
38 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
15 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
32 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
36 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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1answer
38 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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1answer
24 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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127 views

(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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24 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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1answer
69 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
38 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
102 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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