Linked Questions
34 questions linked to/from What's the difference between principal component analysis and multidimensional scaling?
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How does Principal Coordinate Analysis (PCoA) work, as compared to PCA? [duplicate]
I am familiar with PCA from Making sense of principal component analysis, eigenvectors & eigenvalues where you either normalize the data (to standard normal or ...
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When should you call it PCoA vs. PCA [duplicate]
I'm reviewing a paper. In it the authors use a genetic distance metric to created a distance matrix between subjects, then they run classic MDS on this distance matrix. Throughout the MS they call ...
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Representing a distance matrix in the plane [duplicate]
I've worked with observations as vectors with both continuous and categorical variables. In both cases one can use dimensionality reduction techniques such as PCA (in the latter case through ...
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How does Factor Analysis explain the covariance while PCA explains the variance?
Here is a quote from Bishop's "Pattern Recognition and Machine Learning" book, section 12.2.4 "Factor analysis":
According to the highlighted part, factor analysis captures the covariance between ...
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Understanding distance correlation computations
As far as I understood, distance correlation is a robust and universal way to check if there is a relation between two numeric variables. For example, if we have a set of pairs of numbers:
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What is the role of MDS in modern statistics?
I recently came across multidimensional scaling. I am trying to understand this tool better and its role in modern statistics. So here are a few guiding questions:
Which questions does it answer?
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PCA and exploratory Factor Analysis on the same dataset: differences and similarities; factor model vs PCA
I would like to know if it makes any logical sense to perform principal component analysis (PCA) and exploratory factor analysis (EFA) on the same data set. I have heard professionals expressly ...
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Performing PCA with only a distance matrix
I want to cluster a massive dataset for which I have only the pairwise distances. I implemented a k-medoids algorithm, but it's taking too long to run so I would like to start by reducing the ...
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How to project high dimensional space into a two-dimensional plane?
I have a set of data points in a N-dimensional space. In addition, I also have a centroid in this same N-dimensional space. Are there any approaches that can allow me to project these data points into ...
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What is meant by PCA preserving only large pairwise distances?
I am currently reading up on t-SNE visualization technique and it was mentioned that one of the drawbacks of using principal component analysis (PCA) for visualizing high-dimensional data is that it ...
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Distances in PCA space [closed]
I'm working on a project involving PCA, and my knowledge up till now with this method is quite good. My work involves finding nearest neighbors (having the least Euclidean distance) to a particular ...
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Efficient way to compute distances between centroids from distance matrix
Let us have square symmetric matrix of squared euclidean distances $\bf D$ between $n$ points and vector lengthed $n$ indicating cluster or group membership ($k$ clusters) of the points; a cluster may ...
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Q-mode vs. R-mode PCA
I have some doubts on Q-mode and R-mode principal component analysis (PCA). I've read from different sources that:
Q-mode PCA is equivalent to R-mode PCA of the transposed data matrix!
Q-mode PCA (...
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What are shortcomings of PCA as a dimensionality reduction technique compared to t-SNE?
I have been reading and using tSNE which is able to preserve the neighbour around the a point in high dimension compared to PCA. For example I have these embeddings in 128 dimensional created by a ...
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What is the difference between MDA and LDA and how is it applied in SciKit-learn?
According to this paper, Canonical Discriminant Analysis (CDA) is basically Principal Component Analysis (PCA) followed by Multiple Discriminant Analysis (MDA). I am assuming that MDA is just ...
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