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Questions tagged [dimensionality-reduction]

Refers to techniques for reducing a large number of variables or dimensions spanned by data to a smaller number of dimensions while preserving as much information about the data as possible. Prominent methods include PCA, MDS, Isomap, etc. The two main subclasses of techniques: feature extraction and feature selection.

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is random projection a linear or non-linear feature extraction method?

The dimensionality reduction has two different types: feature selection and feature selection. As far as i know, the random projection cannot be a feature selection method. Therefore, is it a linear ...
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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: ...
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101 views

Can t-SNE help feature selection?

I'm training a fully connected feed forward neural network for regression. Given one training example $(x_i, y_i)$, I need to convert the raw representation $x_i$ into an invariant representation $...
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What is the problem with $p > n$?

I know that this is the solving system of linear equation problem. But my question is why it is a problem the number of observation is lower than the number of predictors how can that thing happen? ...
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What distinguishes the different dimensions of t-Distributed Stochastic Neighbor Embedding (tSNE)

I understand conceptually how the tSNE algorithm works in one dimension. But I am confused how this works for 2 or more dimensions (let us call the number of dimensions of the lower dimensional space ...
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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. ...
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21 views

Minimum Input Dimension for Autoencoder Neural Network

Model: Assume we want to learn patterns using an autoencoder neural network. In the simplest case, such a network is "shallow" with 1 hidden layer, takes a $d$-dimensional numerical input vector $x$, ...
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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). ...
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How to find orthogonal projection of a d-dimensional point, d>2 on Fisher's discriminant?

We know that the number of Fisher's discriminant to find for classifying data in given dimension d into output classes K, is min(K-1,d) Using the iris dataset as an example, we know that d=4 (petal ...
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Searching intersection of elements in subsets with approximation

Task: Having a large number of transactions that consists of distinct elements from one large set $S$ I need to find transactions in which items have intersection with more than 20% of items in ...
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Nipals and prcomp differences and data size relationship [closed]

I'm pretty new in R and data analysis and I found a strange behaviour which I wanted to discuss with you. I had a 486 observation of 5000+ variables and I want to cluster them with Mclust. Each value ...
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Is “random projection” strictly speaking not a projection?

Current implementations of the Random Projection algorithm reduce the dimensionality of data samples by mapping them from $\mathbb R^d$ to $\mathbb R^k$ using a $d\times k$ projection matrix $R$ whose ...
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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. ...
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Using UMAP or other non-linear dimension reduction techniques on response variables prior to learning?

Background Suppose you have a training set where the response measurements are some $N$-dimensional vectors of related measurements - in my specific case, they happen to be cell viability scores for ...
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Log-transformation of Compositional Data

I am dealing with compositional data, in a high dimension. Each sample I have behaves like: $$ {S}^D=\left\{\mathbf{x}=[x_1,x_2,\dots,x_D]\in\mathbb{R}^D \,\left|\, x_i>0,i=1,2,\dots,D; \sum_{i=1}...
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Implementation of Compressed Regression (examples?)

I found several articles discussing compressed regression, whether in the bayesian framework or ordinary LS/GLM (1, 2, 3 and others). The idea seems simple, the model becomes $$ Y = \Phi X \beta + \...
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Terminology: the “t-distribution” in t-SNE?

I am trying to learn the dimensionality reduction using t-SNE technique. After some videos and explanation I understood the idea behind it. But I am not getting where the "t-distribution" is used ...
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t-SNE for finding nearest neighbors

I had a question about dimensionality reduction for finding nearest neighbors and was hoping someone could help me out here. Suppose I have good features for images, say penultimate layer features ...
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How to reduce sample size in predictive model?

I am working on a regression model, which purpose is solely to predict new data. My training dataset contains around 200 measurements and with a best subset regression i was able to find good models ...
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Transforming Dimensions of PCA/MCA/FAMD/MFA to strict positive values

I´m curious to know if the outcome of a given principal component analysis or other related methods can be transformed to strict positive values. I know every dimension is represented by a continuum ...
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Interpreting t-SNE dimensions in terms of the original features [duplicate]

I have a dataset of 223 multilabels. I'm sure some of those labels are correlated so I want to merge them. For that I'm using sklearn tSNE to reduce the dimensions. How can I "describe" the new ...
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Intuition behind perplexity parameter in t-SNE

While reading Laurens van der Maaten's paper about t-SNE we can encounter the following statement about perplexity: The perplexity can be interpreted as a smooth measure of the effective number of ...
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Dimensionality reduction: include labels?

Lets say I have something like the iris dataset, the columns are petal length, petal width, ...
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1answer
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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: ...
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Does it make sense to run lasso to select features for neural network training?

I want to train a neural network for regression. $$\Bbb R^{2800} \rightarrow \Bbb R^{1}$$ The dimension of feature vectors is $2800$. The figure is an illustration of one of the feature vectors. ...
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Dimensionality reduction with least distance distortion

Question: Could I find a dimensionality reduction algorithm without or with minimal distance (cosine) distortion? Background: I would like to visualize in 2D a sample of news texts for which I also ...
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Differences between t-SNE and SOM

I have some high dimensional data and I want to reduce it to 2 dimensions for visualization. The goal is to color the points in this 2D space to see whether there is any clustering due to different ...
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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 (...
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Principal components: Can I interpret PCA as essentially a change of basis

I was hoping that someone could simply validate or correct my interpretation of Principal Components Analysis. There are a lot of questions on this site about Principal Components analysis--some ...
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How to cluster and reduce dimensionality of Barthel scale data

I 'd like to cluster subjects with their Barthel scale. It looks like below: Since the Barthel scale data is an ordinal scale and a likert-type rating data (given number of points assigned to each ...
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2answers
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How to find the minimum set of vectors?

I have a set of vectors. For simplicity, suppose there are $n=20$ vectors and each has $p=5$ elements. These vectors are generated from some experiments, and so it is not very apparent how they are ...
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Example for Principal Component Analysis

Where principal component analysis can potentially be used ? some examples with some explanation would be great
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38 views

What does ICA return?

I am confused with ICA. With PCA I understood that it always gives the components with maximum variance. What does ICA return? Does it return components with maximum independence? How to find best ...
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1answer
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Self-organizing map dimension

I just have a question on the chosen dimension of Self-organizing map. Typically, an SOM has a dimension of 2 or 3 but rarely larger than that. Is there a particular reason why this is the case?
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Gaussianity and Whitening in ICA - The feeling and intuition behind it

I understand what ICA does at a high level but in the cocktail party problem context. All the examples, articles I have read take a similar problem to explain ICA where the aim is to derive the ...
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optimality of LDA dimensionality reduction

typically when you do dimensionality reduction using LDA, you select $n_{class}-1$ vectors with largest eigenvalues as discriminants given the fact that you only need $n-1$ dimensions to classify $n$ ...
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How do I get the density of a region in a vector space?

I have a simple problem, which I think must have an easy solution. I have a vector space say with a 1000 dimensions for each vector. Now, I have a large number of sample vectors from this vector ...
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42 views

Dimensionality reduction for multivariate time series data?

I'm trying to perform dimensionality reduction on a multivariate time-series data set which gives information on the medical history of a group of 12000 patients. The data contains 19 different ...
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Linear Discriminant Analysis as Dimensionality Reduction very sensitive to Training Set size

I'm working with supervised classification of object-based satellite imageryand currently investigate different dimensionality reduction methods on their suitability to this application. As part of my ...
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What kind of PCA is best for my data?

I'm using sklearn to reduce the dimensionality of an object.This scan is 3D, with shape [15,15,15] and contains mostly zeros, altough the non-zero values are usally clustered together in some groups. ...
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PCA Dimension Reduction for Cox Proportional Hazard Model

I am trying to figure out if it is possible and appropriate to use PCA to reduce the dimensions of my dataset? A little background - I have survival data with 8 covariates. I have run a Pearson's ...
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Variability in categorical variables while reducing dimensionality

If a dataset has a variable with categorical values (A, B, C, D, E, F), would converting them to numeric and breaking them down into separate columns (for example, column A that will have 1s for all ...
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PCA: mean of marginal distribution of high-dimensional vector

Consider the following probabilistic model: $$p(x) = \mathcal{N}(0, I_d), \ x \in \mathbb{R}^d$$ $$p(y|x) = \mathcal{N}(Wx + \mu, \sigma^2I_D), \ y, \mu \in \mathbb{R}^D, W \in \mathbb{R}^{D\times d}...
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Performing PCA on properly scaled data [duplicate]

I would like to perform PCA on a dataset, however, not all of the data is in the same scale. Some variables are Height, Weight, Age, while others are Dribbles per Game, Shots per Game, Blocks per Game....
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Non-negative matrix factorization (NMF) on mixed data using 1-hot encoding

From a standpoint of interpretation, can I use NMF on one-hot encoded categorical data for dimension reduction? I have mixed data and was thinking about one-hot encoding the categorical features and ...
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Why with two classes, LDA gives only one dimension?

I am working with dimensionality reduction algorithms. Linear Discriminant Analysis (LDA) is a supervised algorithm that takes into account the class label (which is not the case of PCA for example). ...
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Comparing PCA representations between low-pass and high-pass filtered time-series data

I am currently trying to reduce the number of variables I input into a vector autoregressive (VAR) model. For those that don't already know, VAR models are used on time-series data. My primary concern ...
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Bag of Visual Words: is feature extraction even needed?

I'm currently implementing a BoVW as part of my lab project. The steps the algorithm used are as follows: spliting all photos into patches cluster these pathces using K-means based on pixel values of ...
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Regression with multidimensional output variable Y

Say we have an $N \times q$ matrix $Y$ with $N>q$. Also, we have an $N \times p$ data matrix $X$. We are interested in a model of $Y = X \times W + \epsilon$, where $W$ is a $p \times q$ matrix ...
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Clustering by same random projection

I have $N$, $1024$-dimensional vectors. I want to cluster them by some similarity. Given the high dimensionality, standard metrics won't work. I tried a few Approximate Nearest Neighbor ...