Questions tagged [pca]

Principal component analysis, a technique for dimensionality reduction.

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37 views

Dot product and linear regression

I'm studying PCA and my professor said something about finding the linear regression by doing the dot product of both axis. Could someone explain to me why? The dot product returns a number. What's ...
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33 views

whether to use normalization while performing PCA?

I have an excel file containing a table where I registered the frequency of three linguistic phenomena in 72 poems. Since the poems have different lengths I normalized the results dividing each value ...
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9 views

How to do feature reduction for a log-linear regression model

I'm building a log-linear regression model and I have 18 different variables in my model. 13 out of 18 variables I'm using are hot-encoded variables for holiday, e.g. showing which holiday it is. I ...
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18 views

How to check PCA in Orange?

Input file: T X Y A 1 3 A 5 8 B 7 6 B 4 2 T is categorial target variable I calculate PCA in Orange: components X Y 1 PC1 -0.707107 -0.707107 2 PC2 0.707107 -0.707107 T PC1 PC2 1 A 1.58032 -...
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31 views

Help why to apply PCA here

Lets say we have a dataset of 9 dimensional points and I want to apply k-means algorithm. I was studying an example where they apply PCA before fitting the data into the clustering algorithm. The ...
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19 views

Order of eigenvalues when using different methods

I'm doing PCA in a covariance matrix where each column and row represents tenors of the yield curve. I have coded the Jacobi rotation method and I also have a QR algorithm based on numpy.linalg.qr in ...
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1answer
55 views

PCA, covariance, eigenvector matrix and rotation [closed]

I am following the Coursera NLP specialization, and in particular the lab "Another explanation about PCA" in Course 1 Week 3. From the lab, I recovered the following code. It creates 2 ...
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How many pictures are needed to generate eigenfaces that correctly represent the input faces?

I would like to show how eigenfaces work. Therefore, I would like to take 5 pictures of the person who is using my code and then after PCA reduction I would like to show the eigenfaces which represent ...
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I need help in PCA results using WEKA Tool [closed]

I'm working on an experiment using KDD'99 cupset I have 42 features. the paper I 'm comparing with concludes that 3 features with precision ..% ok are the best subset to identify the attack X. In my ...
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17 views

SVD based method for predicting missing values in future experiments

I would like to pose my question in terms of a hypothetical example, where I have a matrix: 11 12 13...1m 21 22 23 ..2m . . . 3m . . . . . 11 n2 n3 . nm The matrix has no missing values. ...
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31 views

Do i need sort the data by the eigenvalues with descend (PCA)

after computing the eigenvectors and eigenvalues. the eigenvectors sort by eigenvalues on descending. data * eigenvectors = transformed data how about the standardized raw data, do I need to sort them ...
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2answers
47 views

Principal components analysis need standardization or normalization?

Principal components analysis need standardization or normalization? After some google, I get confused. pca need the scalar be same. So which should I use. Which technique needs to do before PCA? Does ...
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1answer
31 views

PCA ? after the transformed data, are they still same with original data, (if maintain same dimensional) [closed]

after the step of pca, I try to plot them (see pic) example in the original (A) 2 features => pca 2 features if I do not reduce any dimensional data, are they still have the same meaning of these ...
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19 views

With NN Model data preprocessing, is One Hot Encoding WITH PCA a good or bad idea?

With Tabular Data (containing both continuous and categorical data) preprocessing, does the One Hot Encoding of Categorical Features help or hinder the effectiveness of PCA prior to Neural Network ...
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1answer
38 views

Using PCA as features for production

I struggle with figuring out how to proceed with taking PCA into production in order to test my Models with unknown samples. I'm using both an One-Hot-Encoding an an TF-IDF in order to classify my ...
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16 views

Dimensionality Reduction for Function Fitting method using Kernels

I have a set of continuous noisy measurements $x_i \in R^n$ with $i=1,...,N$ for which I know the value range, i.e. $x_{min} \leq x_i \leq x_{max}$. Corresponding to the measurements, I have a set of ...
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1answer
59 views

What are the model parameters in PCA? [closed]

I've been asked to report the number of parameters to be learned in a PCA model. This answer implies that parameters do exist in PCA, but does not explain. Software packages often report the number of ...
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2answers
34 views

How to perform Anomaly Detection on a force profile?

I have a set of force profiles of an industrial machine. I'm trying to develop an algorithm that tries to understand when a new profile is "anomalous" with respect to the ones in "...
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1answer
34 views

I want to run PCA on a data set that will be aggregated by country. Should I aggregate the data before or after I standardize the data, and why?

Basically the title asks my question. I have the results of a survey that was filled out by people from different countries. I have been asked to analyze the data using PCA and see what findings I can ...
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1answer
70 views

Why are eigenvectors produced by np.linalg.eig different than the PCA components stored in the instance of the PCA object?

I am trying to understand why eVec (produced by np.linalg.eig) is different than ...
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1answer
55 views

How do I determine which variables contribute to the 1st PC in PCA?

Given the coefficients of PC1 as follows for each variable (0.30, 0.31, 0.42, 0.37, 0.13, -0.43, 0.29, -0.42, -0.11) which variables contributes most to this PC? Does the sign(+/-) matters or ...
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1answer
41 views

Ordering of standardization, pca, and/or tfidf for neural network

I have 60k rows of text data. I have tokenized it into 55k columns. I am using a neural network to classify the data but have some questions about how to order my preprocessing steps. I have too much ...
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0answers
23 views

Dealing with high dimensionality datasets

I have data of dimensionality (25000, 100, 500) i.e. 25000 rows each consisting of a 2 dimensional 100 X 500 matrix. Currently I am only applying CNN for ...
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24 views

need to create 4 group using the data

i have to create four group using this data :- ...
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1answer
20 views

Clusterize Spectrum

I have pandas table which contains data about different observations, each one was measured in different wavlength. These observsations are different than each other in the treatment they have gotten. ...
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0answers
74 views

PCA and SVD main difference

I have spent multiple days trying to grasp the concept of the PCA and SVD. But I still have a couple of confusion about the difference between SVD and PCA. I watched Steve Brunton Youtube SVD playlist ...
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56 views

The Merits of Feature Reduction Routines

I am interested in learning what routine others use (if any) for Feature Reduction/Selection. For example, If my data has several thousand features, I typically try {2,3,4} things right away depending ...
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19 views

PCA and the relationship between number of samples and number of dimensions

I'm performing PCA on a high dimensional dataset: 800,000 samples by 300 features for which my aim is to identify clusters using Kmeans. After keeping only the numerical features that weren't noisy or ...
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1answer
45 views

Why is Regularization after PCA or Factor Analysis a bad idea?

I have done Factor Analysis on my data and applied various machine learning models on it. I particularly find it giving high MSE value for Ridge and Lasso Regression compared to other models. I want ...
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28 views

create and use weights in Python to perform weighted correlation and PCA

I need help with the principal components’ analysis code below in 3 ways: Write Code to create RIM (RAKE) weighting. I’m trying to ensure that using the code below, I get exactly the same results than ...
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1answer
30 views

Suspiciously good accuracy using neural network

I have a dataset from EEG data that is 24 features (24 electrodes) and 88000 samples with 3 classes, it is normalised and everything and had some noise filtered out via bandpassing. When I classify ...
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1answer
127 views

Calculating distance between data points when there are more than 3 features in KNN algorithm

I've been reading about K-nearest neighbors algorithm and want to clarify few things. If we have 2 features we could simply plot it on 2-d plane and calculate distance by using euclidean distance or ...
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36 views

How to plot centroids and clusters resulting from a KMean model based on a text variable

I hope you can help as after several attempts, I'm no longer sure I can get a decent result. I have a text corpus made of several documents, like the one below (which has been simplified for the sake ...
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1answer
67 views

PCA for dimensionality reduction with simultaneous clustering

so, let's say I have a set of 3D points. Let's say these points lie more or less on a plane that is embedded in the 3d space, then I can use PCA to 'compress' these 3D points to 2D coordinates on that ...
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1answer
60 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
50 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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8 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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17 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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33 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
43 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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16 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
538 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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2answers
182 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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4answers
59 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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2answers
35 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
40 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
29 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
29 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
180 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
47 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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