Dimensionality reduction refers to techniques for reducing many variables into a smaller number while keeping as much information as possible. One prominent method is [tag pca]
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How can you make linear discriminant analysis reduce dimensions to the number of dimensions you are looking for?
Let's say I have a $m \times n$ matrix where $m$ is the number of points and $n$ is the number of dimensions. I would like to give a target dimension parameter which is let's say d. d can be a set of ...
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Interpretation of Scree plots and Boruta Outcomes
I have 37 features in my dataset. I used Boruta package in R and according to its analysis, all the features are "important" and should be retained. I examined this result of Boruta and found that if ...
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Nonlinear manifold learning and distances between projected points
If I use a manifold learning method to project some data points into a low dimensional space, what will be the distances between the projected points? Can I use Euclidean distance? If the distances ...
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How to interpret factor scores saved as Reg variables in SPSS?
How do I interpret the factor scores that I save as Regression variables with SPSS? I have 30 attribute variables with reponses such as extremely important, very important, important, not that ...
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Reducing the dimension of an embedding
Let $O \in \mathbb R^{p\times m}$ be a data matrix of observations.
Suppose we are given a model $\mu : \mathbb R^n \rightarrow \mathbb R^m$ which is able to approximately fit the observations. Fix ...
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Interpreting “elliptic” shape in visualization of high-dimensional dataset, e.g. COIL-20?
There exist an image dataset referred to as COIL-20. There are several paper that describes different dimensionality reduction methods and apply them to produce a 2d plot of this dataset. These ...
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Dimensionality reduction with locality-sensitive hashing
I have a conceptual understanding of locality-sensitive hashing (LSH) in relation to near-neighbour search. However, the articles that I have read so far seem to gloss over the details of how LSH can ...
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Factor analysis for categorical target variable
I'm doing some research into factor analysis and I've hit that barrier where I don't know what search terms to use.
I'm trying to see if something is possible. Basically I have a data set with ~100 ...
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Dealing with non-flat, unbounded data on a single observation
I'm currently trying to generate a number of (rudimentary) models to perform classifications (or get probability distributions through classifications).
Let's say that I have a particular video from ...
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Clear description of PCA using SVD
After reading thousands of articles on PCA and SVD, using them in a number of programming frameworks and even implementing similar techniques (like Random Indexing) I found out that I still have ...
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Supervised dimensionality reduction-applications
What are the applications or advantages of dimension reduction regression (DRR) or supervised dimensionality reduction (SDR) techniques over traditional regression techniques (without any ...
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How to reduce dimensionality of vector?
I have a set of vectors. I'm working on ways to reduce a n-dimensional vector to a unary value (one-dimensional), say
(x1,x2,....,xn) ------> y
This single value needs to be the characteristic value ...
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How to interpret this output in Dimension Reduction?
I'm running Linear Discriminant Analysis on a dataset and then performing clustering on it. I'm reducing it to dimensions 2,6,10. On comparing metrics like Accuracy and Normalized Mutual Information, ...
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Variation explained by single variable
I’m trying to find a way to measure how much a single variable ‘summarizes’ a full set of continuous variables. For instance, in a PCA the first principal component will explain a certain percentage ...
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52 views
Reduce matrix size for hierachical clustering
I have 500.000 measurements (rows) for 20 samples (columns) and I want to do hierarchical clustering to see of I can detect samples groups in the data. Clustering on the columns works fine in R:
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Finding the projection used in multidimensional scaling
Background
I have a set of data points in high-dimensional (512D) space that I wish to map to 2D for visualisation. I am interested in observing in 2D the (approximate) relative distances between the ...
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Orthogonalization for dimensionality reduction
There are some methods like singular value decomposition (SVD), principal component analysis (PCA), factorial analysis and many more that are used to reduce a high-dimensional dataset into fewer ...
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Error analysis and measurement of uncertainty of the dimension reduction methods
I have got a problem of dimension reduction methods: how to do an error analysis and uncertainty measurement for the dimension reduction results?
For example, the Laplacian Eigenmaps method solves a ...
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Dimension reduction: preserving original characteristics of data or not?
I am new in dimension reduction techniques, I need to know what are the advantages and disadvantages of
Preserving the original pattern of data after making data reduction
Making reduction and ...
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170 views
Dimensionality reduction on binary matrix
Is there a way to do dimensionality reduction on a binary matrix. From what I know, Principal Components Analysis does not work. Any suggestions on this matter.
Edit: It's not a symmetric matrix.
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Why would concatenating feature vectors lead to better estimates?
I wish to estimate the state of a system from two separate and disparate observations. A simple approach that I have seen in some literature is to combine the feature vectors (observations) by simply ...
