Questions tagged [pca]

Principal component analysis, a technique for dimensionality reduction.

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Eigenvalues of covariance matrix are negative

I'm working on the PCA of the mnist dataset, and I get a very strange result, I created a matrix whose rows are flattened mnist images, When I try to compute the eigenvalues of the covariance matrix, ...
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1answer
26 views

How to interpret the original time series approximated using principal component analysis? [closed]

I've read some posts about PCA applied on time series, but still a bit confused and I have the following questions(Suppose I am working with a time series of the return of 50 industries and I want to ...
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20 views

Is there a way to implement nested features in unsupervised models?

Our project has built an unsupervised model that uses data about a number of companies. Some of these companies are public and some are private. The ones that are public have much higher financial ...
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15 views

Difficulty understanding the dimension differences in kernel PCA

In Kernel PCA, the kernel trick works because we can show that there is an equivalency between eigenvectors of the kernel matrix and eigenvectors of the covariance matrix. I know the math to go from ...
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60 views

Calculation of PCA

Consider the following data set : Now we need to calculate the principal component analysis for this data. Here are the eigenvalues and eigenvectors calculated for the covariance matrix of this data :...
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33 views

Considerations to take into account when clustering

My idea is to use clustering to perform stock segmentation based on risk, building different risk levels that might adapt better to different kind of users. Hence I have computed different risk ...
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2answers
64 views

Is there any different between feature selection and pca? If there is could anyone please kindly explain for me please?

First of sorry for asking a possibly beginner question, but i don't understand pca seems to be the same as feature selection, but when i search online they seems to be talked differently. What people ...
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1answer
85 views

Perform PCA on columns of different length

I have about 20-30 columns, all with different lengths. The first column has 25000 rows, the second column has 19000 rows, and it's different for all the columns. All are the survey data with 0 (No),1(...
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1answer
227 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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52 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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18 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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41 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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1answer
147 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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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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23 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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37 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
101 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
37 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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2answers
84 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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1answer
69 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
40 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
73 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
152 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
434 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
138 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
28 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
22 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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148 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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0answers
62 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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1answer
74 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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1answer
41 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
519 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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145 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
86 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
71 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
55 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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0answers
24 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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0answers
77 views

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

I use pySpark and set my configuration like following: ...
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3answers
1k 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
704 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
156 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
72 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
41 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
32 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
49 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
209 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
493 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
39 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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1answer
235 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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