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Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3171–3180 of 3304 papers

TitleStatusHype
Supervised Quadratic Feature Analysis: Information Geometry Approach for Dimensionality ReductionCode0
Solar Flare Forecast: A Comparative Analysis of Machine Learning Algorithms for Solar Flare Class PredictionCode0
Supervised Stochastic Neighbor Embedding Using Contrastive LearningCode0
Solving ARC visual analogies with neural embeddings and vector arithmetic: A generalized methodCode0
Solving Interpretable Kernel Dimension ReductionCode0
Cross-Temporal Spectrogram Autoencoder (CTSAE): Unsupervised Dimensionality Reduction for Clustering Gravitational Wave GlitchesCode0
CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstructionCode0
Affective Manifolds: Modeling Machine's Mind to Like, Dislike, Enjoy, Suffer, Worry, Fear, and Feel Like A HumanCode0
Bounds on Representation-Induced Confounding Bias for Treatment Effect EstimationCode0
Locally Linear Image Structural Embedding for Image Structure Manifold LearningCode0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified