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33 views

FastICA results not exactly consistent on repetition

I have asked this on stack overflow but couldn't get an answer. I am using the fastICA implementation in R. Example code: ...
1
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1answer
32 views

How to use PCA to detect outliers?

A PCA will reduce the dimensionality of the original data and construct a subspace generated by eigenvectors of which each represents the (next) highest variance to explain the data. Let's start at ...
3
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1answer
142 views

How can one implement PCA using gradient descent?

I have to implement PCA using gradient descent and stop at convergence. I am not able to find the objective function. I know that the aim of PCA is to reduce the $n$-dimensional matrix to $k$ ...
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0answers
22 views

Can I use the matrix $U$ instead of the matrix $V$ in Principal Component Analysis?

I'm taking Andrew NG's Machine Learning Course and got to the part of Principal Component Analysis. Andrew's implementation of PCA aroused 2 questions for me. 1. Let's say that we have the data ...
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1answer
28 views

What does PC1 mean in prcomp output?

I'm having trouble trying to understand the output of the prcomp function from package stats in ...
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0answers
15 views

Finding PCA-like directions in feature space that maximise sensitivity to a target variable

I have a fairly large space of feature variables in which I want to build a predictor for a target variable. My input dataset for training the predictor are sampled from the space using a mix of log ...
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0answers
15 views

How to combine PCA scores? [duplicate]

I was wondering if anyone knew how to combine the different PCA scores. In my dataset I have 3 PCs that explain more than 10% of the variation of shape of a bone and each one explains different ...
-1
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1answer
29 views

Is it meaningful to reduce dimension from 300 to 100 by PCA?

as I have only learned PCA for a short while, some problems occured when I faced practice. I am willing to accept solution or advice for the following content and great many thanks for anyone's kindly ...
1
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2answers
47 views

Why is there a reconstruction loss in PCA with orthonormal eigenvectors?

I've already read How to reverse PCA and reconstruct original variables from several principal components? and I understand conceptually and visually why there has to be a reconstruction loss. ...
0
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1answer
51 views

Finding the variables that explain a label through LDA and PCA

I have a dataset of about 200 continuous variables with an also continuous target variable. I want to find those predictors that explain target values that are below 50. To do that I create an ...
2
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1answer
47 views

Intuition About Principal Component Directions

I am trying to really get a deep understanding of PCA. From my understanding, a principal component is defined as $$\mathbf{z}_k = \phi_{1,k} \mathbf{x}_1 + \ldots + \phi_{p,k} \mathbf{x}_p = \mathbf{...
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1answer
30 views

Non-linear dimensionality reduction for detecting coordinate systems [closed]

I am trying to find a way to automatically find the appropriate coordinate system for a physical problem. For example, in the case of a simple pendulum, polar coordinates are the most appropriate ...
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0answers
19 views

Different number of Eigen/Singular values from PCA and SVD

My understanding is that a SVD done on a raw data matrix M and a PCA done on its covariance matrix C should return the same eigen/singular values. I have a 2736 x 356 data matrix and am using the ...
2
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1answer
127 views

When to prefer PCA over regularization methods in regression?

When dealing with the curse of dimensionality, regularization methods seem to be clear in their intuition. All "regularization" methods can be seen as a "squeezing" of one's variables towards ...
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0answers
23 views

Problem with Principal component (PCA) and Partial least squares (PLS) using R

I'm trying to reduce highly dimensional data with factor methods. I'm using Principal component analysis and Partial least squares. From these methods I'm using the first component as a Common factor ...
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0answers
17 views

Subtraction between features before performing PCA and training a classification model

In my dataset, I've created moving averages for historical indicators. Is it pertinent to perform subtraction between those moving averages before PCA? My stats/calculus feeling: as PCA is a ...
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0answers
35 views

EOF/ PCA: do I need to detrend my multivariate time series before finding Empirical Orthogonal Functions?

I am familiar with Principal Component Analysis, but I have recently been asked to find the Empirical Orthogonal Functions of a multivariate time series and I am not sure if what I need to do is just ...
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1answer
67 views

Clustering users with very sparse data

I have a dataframe of the form ...
0
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1answer
23 views

Positioning multivariate data in a 2-dimensional space (with PCA)

I have multidimensional data. (11 columns - attributes , 150K rows - number of data). It is slightly sparse-alike data, for example, which means one datum has numeric values like (0, 0, 6.5, 0, 0, 7.5,...
2
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0answers
55 views

How to conduct a principal component analysis on data set with large number of zeros

I have data for percentage cover of plant species in 500 sites. There are columns for 30 different species in the data set and I would like to drastically reduce this down to a manageable number of ...
0
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0answers
21 views

Appropriate dimensionality reduction technique for a small, but high-dimensional sample

I am attempting to conduct some multivariate analysis on a dataset I've been given with a sample size (n) of 23 and a feature number (p) of ~800. I would like to use dimensionality reduction, but ...
0
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0answers
43 views

Principal Component Analysis - Why Use Eigenvectors of the covariance matrix? [duplicate]

In PCA we start with a dataset and we reduce its dimensions by giving it new features that are each a linear combination of the original features of the dataset, and only keeping the ones with maximum ...
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0answers
32 views

When to use PCA vs LDS vs nMDS for microbiota dataset?

I'm trying to understand the certain situations in which you would use the 3 above ordinance/rank tests over the other in terms of microbiota count data. Typically, I have been told to use nMDS over ...
0
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0answers
41 views

How to identify and reduce question overlap and redundancies in a survey? (remove questions asked for a more concise survey, w/o losing information)

Suppose I have a survey that contains 30 items. The items ask about the relationship between the respondent and their family, in many different realms. For example, the strength of the connection ...
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0answers
16 views

Dimensionality reduction before clustering cosine data values causes a change of scale

In my experiment, I am doing hierarchical agglomerative clustering of texts (parameters: cosine, average). My features matrix is very sparse, so I considered PCA as dimensionality reduction technique. ...
0
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0answers
17 views

Dimension Reduction on Data with both Spatial and Non-Spatial Variables to Train a Logistic Regressor for Cross Sectional Time Series Data

I need some help on how to process and analyse a study of mine. I'm running a study on mice to look at the effect of diet on cells over a series of time. My mice are divided into two groups, one group ...
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0answers
21 views

How do you actually use PCA in MATLAB?

I'm trying to use the pca command in MATLAB for dimensionality reduction. I know that [U, V] = pca(X) will yield the principal components in U and the scores in V,...
2
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0answers
50 views

Principal Components' relation with variables having lower variance

This is a philosophical question about PCA, and not a direct coding question. I understand that PCA is a dimensionality reduction technique which results in a certain set of PCs, each PC being a ...
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0answers
11 views

Why do I get an error with this data using principal axis factoring but not minimal residual factoring?

I am using n_factors() from the "psycho" package in R to figure out the number of factors for a set of data. When I use prinicipal axis factoring I get the following error: ...
0
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0answers
19 views

How can I recover full dimensional VAR model coefficients after fitting a VAR model to a dimensionality reduced (via PCA) dataset?

I am using PCA to reduce dimensionality prior to fitting a multivariate time-series dataset to a VAR (vector autoregressive) model. Is there any way to convert a PCA-derived VAR model to a full ...
0
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1answer
44 views

Factor Analysis: Single variable contributing to several latent variables

I was wondering whether factor analysis is right tool in my scenario. That is, I have dataset $X = (X_1, X_2, X_3, X_4)$, where $X_i$ denotes a single variable. As far as I understand factor analysis, ...
0
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0answers
230 views

Dimension Reduction for mixed variables

I am working with a dataset which consists of both categorical (14 vars) and continuous variables (5). Each categorical variable consists of a minimum of 2 categories up to 106 categories. The aim ...
0
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0answers
58 views

Number of factors in Factor Analysis of Mixed Data with FactoMineR

I'm trying to perform FAMD with FactoMineR because I want to reduce the dimensionality of my data. My data has 378 dimensions and 34K rows. Around 350 of those dimensions are categorical and the rest ...
1
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1answer
49 views

Approaches to reduce dimensions (feature selection/extraction) with high dimensional count data before running tree based model

My dataset has ~100k samples and 3000 dimensions. The data are counts, anywhere between 0-8 and it's pretty sparse. Because of 'curse of high dimension', I want to shrink the number of variables ...
1
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1answer
319 views

How to interpret PCA coefficients to reduce dimension

I have read about similar questions. I have data which has 68 columns and about 800 samples. The 68. column is the output the rest 67 is the input variables. I want to reduce the size of my input ...
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0answers
22 views

Clarification on quantification of Categorical variables

I have a countries column with 49 levels. I want to quantify it. If I run CATPCA on that column would i be able to get the quantified result. Since CatPCA is like PCA or factor analysis: it extracts ...
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0answers
37 views

Is it possible to weight items differently in a factor analysis?

Suppose I have 100 targets that have been rated by 1000 individuals. I want to perform a PCA on those 100 targets. Now, I'm curious if I were to take some property of the targets into account, how ...
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0answers
29 views

Similarity measure before and after dimensionality reduction or clustering

I have a dataset with 500 000 samples, each sample contains 30 features. The values of the features are in the range 0.0 to 1.0. ...
2
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0answers
29 views

If I recover VAR model coefficients from PCA-derived coefficients, do I need to ensure that the model has zero cross-correlation in the residuals?

I am investigating how to appropriately combine PCA with VAR modeling. I am using PCA to reduce the number of vars I fit to a VAR model, and am attempting to recover the non-dim. reduced coefficients ...
1
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0answers
43 views

Principle Components Analysis - using variance as a variable?

I am following a collaborator’s methods to analyze a set of audio recordings, and I have found that she is using principal components analysis in an unexpected way. I am confused by her approach, and ...
1
vote
1answer
198 views

Does curse of dimensionality also affect principal component analysis calculations?

Based on this post, the Big-O notation for the complexity of calculating principal components analysis is $O(p^2n+p^3)$ for a dataset of size $n$ with $p$ features. I understand that PCA is often ...
7
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1answer
281 views

Using PCA to reduce dimensionality of training and testing data [duplicate]

I've read so many contradicting opinions that I feel like I need to ask the question myself. Say I use PCA on a dataset with 60 variables and find that I can explain 98% of variance with 6 principal ...
0
votes
1answer
215 views

One-hot-encoding gives untractable amount of classes

I'm performing regression on the price of bycicles based on their brand, model and submodel. These features are hierarchical: one model belongs only to one brand but one brand can have many models. ...
0
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0answers
38 views

Determining the Direction of Eigenvectors in PCA [duplicate]

I'm using R to get the principal components for several datasets. An example result, using prcomp yields: ...
0
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0answers
47 views

Feature reduction of Biological time series signals

I have a data set of biological signals (PSG signals); the dimension of the signals is high (850 features for each sample). I am looking for the best way to reduce the dimensionality of the signals. ...
1
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0answers
101 views

What is connection between Pearson correlation coefficient and proportion of variance explained in PCA?

PCA procedure includes SVD of Covariance matrix. Based on eigenvalues we can find a proportion of variance explained by related Principal Components (eigenvectors). ...
-1
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1answer
111 views

PCA influence of duplicates

I am using sklearn IPCA decomposition and surprised that if I delete duplicates from my dataset, the result differs from the "unclean" one. What is the reason? As I think, the variance is the same. ...
1
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2answers
60 views

Dimensionality reduction: include labels?

Lets say I have something like the iris dataset, the columns are petal length, petal width, ...
0
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1answer
128 views

Applying PCA - First two components explain low variance but have high data separation when plotting

Applying PCA on a set of documents gives strange results in terms of the variance explained by the PCs vs the data separation I'm having when plotting the first two principle components. Details: ...
0
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1answer
24 views

Doubt regarding PCA

I have 5 different independent variables, lets name 1 to 5. The 3rd IV has 10 sub-variables under it and 4th IV has 11 sub-variables in it. Whereas other 3 IV's have just two sub-variables (...