Papers with Code 아카이브
Dimensionality Reduction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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Dimensionality reduction is the task of reducing the dimensionality of a dataset.
<span style="color:grey; opacity: 0.6">( Image credit: openTSNE )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Theano/Theano
Theano: A Python framework for fast computation of mathematical expressions
https://github.com/UnofficialJuliaMirror/MRMissing.jl-d89fa356-2490-5e87-a4cc-d004309f6659
https://github.com/UnofficialJuliaMirror/UMAP.jl-c4f8c510-2410-5be4-91d7-4fbaeb39457e
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/UnofficialJuliaMirrorSnapshots/MRMissing.jl-d89fa356-2490-5e87-a4cc-d004309f6659
https://github.com/UnofficialJuliaMirrorSnapshots/UMAP.jl-c4f8c510-2410-5be4-91d7-4fbaeb39457e
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/VGalata/plsdb
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/VilockLi/SBSC
https://github.com/WeiHuang05/Gaussian-process-latent-variable-model-factorization-for-context-aware-Recommendation
Gaussian Process Latent Variable Model Factorization for Context-aware Recommender Systems
https://github.com/WenjieDu/SAITS
https://github.com/Westlake-AI/Markov-Lipschitz-Deep-Learning
https://github.com/Willcox-Research-Group/operator-inference
Reduced operator inference for nonlinear partial differential equations
https://github.com/XiaoxiangLin/DCN-keras
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
https://github.com/ZhangAIPI/YOPO_MLLM_Pruning
Discover and Mitigate Multiple Biased Subgroups in Image Classifiers
https://github.com/abhilash1910/Deep-Graph-Learning
Graph Laplacian Regularized Graph Convolutional Networks for Semi-supervised Learning
https://github.com/abhilash1910/SpectralEmbeddings
Graph Laplacian Regularized Graph Convolutional Networks for Semi-supervised Learning
https://github.com/abittracher/pytmrc
Dimensionality Reduction of Complex Metastable Systems via Kernel Embeddings of Transition Manifolds
https://github.com/agnesdeng/mixgb
https://github.com/agrubertx/New-NLL
https://github.com/ahmedelmahy/myownvit
https://github.com/ahmedssabir/Textual-Visual-Semantic-Dataset-for-Text-Spotting
https://github.com/ahmedssabir/dataset
https://github.com/ai-how/TIme-series-clustering
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
https://github.com/alex-delavega/umap_jades_2025
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/alexwndm/state-detection-somvae
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
https://github.com/amaiya/causalnlp
https://github.com/amazon-science/fair-pca
https://github.com/anantgupta129/CatBoost-in-Python-ML
https://github.com/andland/logisticpca
Dimensionality Reduction for Binary Data through the Projection of Natural Parameters
https://github.com/anonyme20/nips20
https://github.com/antheamonod/FluPCA
https://github.com/apache/incubator-mxnet
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
https://github.com/arjunastra/diagnosing-tremor-smarthphones-machinelearning
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
https://github.com/arturjordao/arturjordao
Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method
https://github.com/as791/ZOO_Attack_PyTorch
https://github.com/ashleyradford/nba_shooting_analysis
Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.