SVD, PCA, multi dimensional visualization

In summary, the conversation discusses the search for open source multi dimensional data visualization tools and the desire to find a pre-existing solution rather than coding one from scratch. The desired features include the ability to plot points with multi-dimensional coordinates, have three orthogonal views with adjustable parameters, remove outliers by clicking on the graphs, choose basis vectors for the axes, and recompute the SVD or PCA. The suggestion of using Paraview or MayaVi is also mentioned, with the possibility of embedding MayaVi into the PyQt application.
  • #1
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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  • #3
I'm using PyQt. It's great, but it would take some time to code.
 
  • #4
Mayavi is embeddable into your PyQt application.
 
  • #5


I understand your need for a comprehensive and efficient tool for multi dimensional data visualization. SVD (Singular Value Decomposition) and PCA (Principal Component Analysis) are powerful techniques commonly used for dimensionality reduction and data visualization. They allow for a better understanding of high-dimensional data by projecting it onto lower-dimensional spaces while preserving the most important information.

In terms of open source tools for multi dimensional data visualization, there are several options available such as Matplotlib, Plotly, and Bokeh. These libraries offer various customizable features for creating interactive plots with multiple views and the ability to change viewing angles. However, it may require some coding to implement the specific features you are looking for.

Regarding your requirements, I suggest looking into the use of 3D scatter plots and interactive widgets in these libraries. These can provide orthogonal views and allow for changing phi, theta, and distance parameters. Additionally, you can use the point-and-click functionality to slice outliers from the data set. For choosing basis vectors, you may need to compute the SVD or PCA separately and then use the results to plot the data in the desired orientation.

In summary, while there may not be a single ready-to-use tool that meets all your requirements, you can utilize the features of open source libraries to achieve your desired multi dimensional data visualization. I would also suggest exploring different tutorials and examples to get a better understanding of how these tools can be used for your specific data set.
 

What is SVD and how is it used in data analysis?

SVD stands for singular value decomposition and it is a mathematical technique used for data analysis and dimensionality reduction. It decomposes a matrix into three matrices, which can then be used to identify patterns and relationships within the data.

What is PCA and how does it differ from SVD?

PCA, or principal component analysis, is another dimensionality reduction technique that is closely related to SVD. The main difference is that SVD is applied to any matrix, while PCA is specifically used for analyzing the variance in a data set.

Why is multi dimensional visualization important in data analysis?

Multi dimensional visualization allows us to visually represent and understand complex data sets with multiple variables. It can help identify patterns and relationships that may not be apparent when looking at the data in its raw form.

What are the benefits of using SVD and PCA in data analysis?

Both SVD and PCA can greatly reduce the dimensionality of a data set, making it easier to analyze and interpret. They also help identify the most important features or variables within the data, allowing for more efficient and accurate modeling.

Are there any limitations to using SVD and PCA?

One limitation is that SVD and PCA can only be applied to numerical data. They also assume a linear relationship between variables, so they may not be suitable for non-linear data. Additionally, the interpretation of results can be subjective and may require some level of expertise.

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