SVD, PCA, multi dimensional visualization

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Discussion Overview

The discussion revolves around the search for open source tools for multi-dimensional data visualization, specifically focusing on visualizing points with more than 20 dimensions. Participants explore existing solutions and discuss features they would like to implement in their own coding efforts.

Discussion Character

  • Exploratory, Technical explanation

Main Points Raised

  • One participant expresses a need for a visualization tool that allows for three orthogonal plots with adjustable parameters such as phi, theta, and distance.
  • The same participant wants functionality to slice outliers from the dataset interactively through the graphs.
  • There is a desire to select specific basis vectors for the axes in the visualization.
  • Participants mention the need to recompute SVD or PCA and the data's projection after outlier removal.
  • Another participant suggests looking into existing tools like Paraview or MayaVi as potential solutions.
  • A participant notes they are using PyQt for their application but acknowledges the time required for coding the desired features.
  • It is mentioned that Mayavi can be embedded into PyQt applications, which may facilitate the development process.

Areas of Agreement / Disagreement

Participants do not reach a consensus on a specific tool or approach, and multiple suggestions and needs remain unaddressed.

Contextual Notes

The discussion does not clarify the specific requirements or limitations of the proposed tools, nor does it resolve the feasibility of integrating the desired features into existing frameworks.

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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