Improvements for Principal Component Analysis
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enhancement
- Dominant language
- Rust
- Stars
- 4.7k
- Forks
- 334
- Avg merge
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- Merged PRs (30d)
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Description
A plain Principal Component Analysis algorithm was added in https://github.com/rust-ml/linfa/commit/7b6075e2dc9cc1c56ad7cd956bf996d69ce51d20. The next steps should improve upon edge-cases and features.
- implement Roweis Discriminant Analysis which mixes supervised and unsupervised models
- implement sparse PCA. By adding a sparsity constraint (like LASSO) only certain principal components are selected to represent the data
- implement robust PCA to improve robustness to outliers by using a L1 norm instead of the normal Frobenius norm
- (?) implement non-linear PCA (should be similar to diffusion maps except for scaling)
- add tests for edge-cases for very large, sparse or ill-behaving datasets
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the plain PCA implementation introduced in commit 7b6075e2dc9cc1c56ad7cd956bf996d69ce51d20. Choose one listed direction, review its linked paper, and identify the relevant API and test locations in the repository. Done means one defined PCA extension or edge-case test is implemented and covered by tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- rust
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100