SVD, PCA, multi dimensional visualization

AI Thread Summary
Open source options for multi-dimensional data visualization are being explored, particularly for datasets with over 20 dimensions. The desired features include three orthogonal plots with adjustable viewing angles, the ability to interactively slice outliers, and the option to select basis vectors for axes. Additionally, there is a need to recompute Singular Value Decomposition (SVD) or Principal Component Analysis (PCA) after outlier removal. Paraview and Mayavi are mentioned as potential tools, with Mayavi being compatible with PyQt applications. The discussion emphasizes the importance of finding existing solutions before developing a custom visualization tool.
rigetFrog
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I just did some quick searches for open source multi dimensional data visualization, but can't find what I'm looking for.

Before I spend time coding it up, I want to see if some one's done it already.

The data will be points with multi (n>20) dimensional coordinates

1) I want to be able to have three plots with orthogonal views, with the ability to change my phi, theta, distance.

2) Slice outliers from the data set by pointing and clicking on the graphs.

3) choose which basis vectors for the axes

4) recompute the SVD or PCA, and the data's projection on it after removing outliers.
 
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I'm using PyQt. It's great, but it would take some time to code.
 
Mayavi is embeddable into your PyQt application.
 
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