https://github.com/bghojogh/Backprojection
공식 코드Backprojection for Training Feedforward Neural Networks in the Input and Feature Spaces
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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- 보존된 평가 기록
- 6
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/bghojogh/Feature-Extraction-Survey
공식 코드Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review
https://github.com/bghojogh/Fisher-Discriminant-Analysis
공식 코드https://github.com/bghojogh/Fisher-Triplet-Contrastive-Loss
공식 코드Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
https://github.com/bghojogh/Generative-LLE
공식 코드https://github.com/bghojogh/Gravitational-Dimensionality-Reduction
공식 코드Gravitational Dimensionality Reduction Using Newtonian Gravity and Einstein's General Relativity
https://github.com/bghojogh/Image-Structural-Component-Analysis
공식 코드Principal Component Analysis Using Structural Similarity Index for Images
https://github.com/bghojogh/Linear-Quadratic-Discriminant-Analysis
공식 코드https://github.com/bghojogh/Locally-Linear-Image-Structural-Embedding
공식 코드Locally Linear Image Structural Embedding for Image Structure Manifold Learning
https://github.com/bghojogh/MDS-SammonMapping-Isomap
공식 코드Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and Survey
https://github.com/bghojogh/Offline-Online-Triplet-Mining
공식 코드Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches
https://github.com/bghojogh/Principal-Component-Analysis
공식 코드Unsupervised and Supervised Principal Component Analysis: Tutorial
https://github.com/bghojogh/Quantile-Quantile-Embedding
공식 코드https://github.com/bghojogh/Quantized-Fisher-Discriminant-Analysis
공식 코드https://github.com/bghojogh/Roweis-Discriminant-Analysis
공식 코드Roweis Discriminant Analysis: A Generalized Subspace Learning Method
https://github.com/bghojogh/SNE-tSNE
공식 코드Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey
https://github.com/bghojogh/Siamese-Network-Histopathology
공식 코드Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study
https://github.com/bghojogh/Weighted-Fisher-Discriminant-Analysis
공식 코드Weighted Fisher Discriminant Analysis in the Input and Feature Spaces
https://github.com/bideliunian/sdr4d2dreg
공식 코드Nonlinear Sufficient Dimension Reduction for Distribution-on-Distribution Regression
https://github.com/biomedia-mira/chexploration
공식 코드Risk of Bias in Chest Radiography Deep Learning Foundation Models
https://github.com/biomedia-mira/cxr-foundation-bias
공식 코드Risk of Bias in Chest Radiography Deep Learning Foundation Models
https://github.com/bionf/phyloprofile
공식 코드PhyloProfile v2 -- Exploring multi-layered phylogenetic profiles at scale
https://github.com/bioshape-lab/neuralgeom
공식 코드https://github.com/biovault/spidr
공식 코드Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images
https://github.com/blei-lab/causal-text-embeddings
공식 코드https://github.com/bogdantoader/manifoldlearninginplatoscave
공식 코드On Manifold Learning in Plato's Cave: Remarks on Manifold Learning and Physical Phenomena
https://github.com/boyangumn/DCN
공식 코드Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
https://github.com/briviosimone/sae_eys
공식 코드Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective
https://github.com/caesarcai/IRCUR
공식 코드Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank Estimation
https://github.com/caiting123321/AT_NMF
공식 코드https://github.com/canc1993/graces
공식 코드Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data
https://github.com/carlson-lab/encodedSupervision
공식 코드Estimating a Brain Network Predictive of Stress and Genotype with Supervised Autoencoders
https://github.com/cassiebuhler/pods
공식 코드Efficient Solution of Portfolio Optimization Problems via Dimension Reduction and Sparsification
https://github.com/catboost/catboost
공식 코드https://github.com/cauchyturing/Deconv_SAX
공식 코드https://github.com/cern-it-innovation/gqc
공식 코드Guided Quantum Compression for High Dimensional Data Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.