# heucoder/dimensionality_reduction_alo_codes

特征提取/数据降维:PCA、LDA、MDS、LLE、TSNE等降维算法的python实现

Repository: https://github.com/heucoder/dimensionality_reduction_alo_codes
Canonical: https://ross.abutalabs.com/products/dimensionality_reduction_alo_codes
Language: Python
License: Apache-2.0
License Family: permissive
Topics: python, feature-extraction, data-reduction
Last push: 2023-05-10T05:58:24+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2642, "days_push": 1211, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2370, forks 609 (observed 2026-08-28T04:06:41.773644+00:00)

## What it is
A collection of Python demo implementations of classic dimensionality reduction algorithms including PCA, KPCA, LDA, MDS, ISOMAP, LLE, TSNE, AutoEncoder, FastICA, SVD, LE, and LPP. Each algorithm comes with example code, visualization results, and links to reference tutorials.

## Use cases
- learn how PCA works with python code
- implement t-SNE from scratch for visualization
- compare dimensionality reduction algorithms
- understand LLE and ISOMAP manifold learning
- find reference implementations of LDA and SVD
- study feature extraction and data reduction techniques

## When to choose
- learning or teaching dimensionality reduction algorithms
- needing simple, readable reference implementations with visualizations
- exploring which reduction algorithm suits your data

## When to avoid
- production pipelines needing optimized, well-tested libraries like scikit-learn
- needing GPU-accelerated or scalable implementations for large datasets

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, data-visualization
- domain: machine-learning, data-science, tutorials
- platform: python
- tags: dimensionality-reduction, pca, lda, tsne, feature-extraction, demo-code, educational, algorithms

## Member repositories
- heucoder/dimensionality_reduction_alo_codes (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:41.773644+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:35:24.953527+00:00, confidence not recorded.
  - readme: https://github.com/heucoder/dimensionality_reduction_alo_codes (fetched 2026-08-28T04:06:41.773644+00:00, sha 5fcec93cac45)
- Data as of 2026-08-30T08:39:29.467469+00:00.