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 1351–1375 of 3304 papers

TitleStatusHype
Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data—0
Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis—0
Full-dimensional characterisation of time-warped spike-time stimulus-response distribution geometries—0
Contextual Bandits with Sparse Data in Web setting—0
From which world is your graph—0
From which world is your graph?—0
Content-Aware Tweet Location Inference using Quadtree Spatial Partitioning and Jaccard-Cosine Word Embedding—0
Application of advanced machine learning algorithms for anomaly detection and quantitative prediction in protein A chromatography—0
Agtech Framework for Cranberry-Ripening Analysis Using Vision Foundation Models—0
Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA—0
From RGB to Spectrum for Natural Scenes via Manifold-Based Mapping—0
Construction of neural networks for realization of localized deep learning—0
From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning—0
A Powerful Face Preprocessing For Robust Kinship Verification based Tensor Analyses—0
From Players to Champions: A Generalizable Machine Learning Approach for Match Outcome Prediction with Insights from the FIFA World Cup—0
Functional Inverse Regression in an Enlarged Dimension Reduction Space—0
From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices—0
Functional sufficient dimension reduction through information maximization with application to classification—0
Consistent Representation Learning for High Dimensional Data Analysis—0
Functorial Manifold Learning—0
Consistent Estimation of Low-Dimensional Latent Structure in High-Dimensional Data—0
Agriculture Commodity Arrival Prediction using Remote Sensing Data: Insights and Beyond—0
Fusing Vector Space Models for Domain-Specific Applications—0
Fusion of heterogeneous bands and kernels in hyperspectral image processing—0
Cone-Constrained Principal Component Analysis—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