Comments (2)
How did you solve this? I'm experiencing a similar problem.
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I think when I wrote this I was fundamentally misunderstanding how PCA works. The variance values relate to new composite features that are combinations of the original features.
I found this article illuminating: https://blog.exploratory.io/an-introduction-to-principal-component-analysis-pca-with-2018-world-soccer-players-data-810d84a14eab
In that article, he ascribes meaning to the newly created components by graphing each data point in the original dimensions as well as the new composite dimensions.
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Related Issues (20)
- Crashes when a feature variance is 0 HOT 5
- Noob question: How do I project my data into the PCA space? HOT 3
- number of components ? HOT 4
- Mono dimensional arrays: TypeError: First argument must be a positive number or an array HOT 8
- update readme once Matrix has a custom inspect function
- Optimize predictions when nComponents is specified
- Scores HOT 1
- How to use `getExplainedVariance` results? HOT 3
- Reference to the feature names in getExplainedVariance HOT 1
- Add Reconstructed loadings HOT 1
- NIPALS returns nans HOT 1
- Question - Plotting Ability HOT 3
- Migrate project to typescript
- Test
- fix DOI in citation.md once published on Zenodo
- RangeError: Submatrix indices are out of range HOT 4
- Add missing typescript types and move documentation to typescript
- Mixed ES6 and CJS causes issues in some enviroments HOT 8
- Dimension of eigenvectors HOT 9
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