SOTAVerified

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 1226–1250 of 3304 papers

TitleStatusHype
Extração e Classificação de Características Radiômicas em Gliomas de Baixo Grau para Análise da Codeleção 1p/19q—0
Extracting Geography from Trade Data—0
Extracting grid characteristics from spatially distributed place cell inputs using non-negative PCA—0
Extracting lexico-semantic relations from specialized corpora using a word space model (Analyse distributionnelle de corpus sp\'ecialis\'es pour l'identification de relations lexico-s\'emantiques) [in French]—0
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations—0
ABID: Angle Based Intrinsic Dimensionality—0
Extreme compression of sentence-transformer ranker models: faster inference, longer battery life, and less storage on edge devices—0
Extreme Dimension Reduction for Handling Covariate Shift—0
Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning—0
Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification—0
An Investigation of Newton-Sketch and Subsampled Newton Methods—0
Face Recognition using Curvelet Transform—0
Face Recognition using Hough Peaks extracted from the significant blocks of the Gradient Image—0
Facilitate the Parametric Dimension Reduction by Gradient Clipping—0
Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey—0
Factor-augmented sparse MIDAS regressions with an application to nowcasting—0
Factorization of Latent Variables in Distributional Semantic Models—0
Collaborative Homomorphic Computation on Data Encrypted under Multiple Keys—0
An iterative coordinate descent algorithm to compute sparse low-rank approximations—0
Broadband Beamforming via Linear Embedding—0
Factors in Fashion: Factor Analysis towards the Mode—0
FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis—0
Combating Financial Crimes with Unsupervised Learning Techniques: Clustering and Dimensionality Reduction for Anti-Money Laundering—0
Fair and skill-diverse student group formation via constrained k-way graph partitioning—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
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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