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 2076–2100 of 3304 papers

TitleStatusHype
Inductive Regularized Learning of Kernel Functions—0
Inference and Interference: The Role of Clipping, Pruning and Loss Landscapes in Differentially Private Stochastic Gradient Descent—0
Inference for Regression with Variables Generated by AI or Machine Learning—0
Influ\^encia de T\'ecnicas N\~ao-supervisionadas de Redu \~ao de Dimensionalidade para Organiza \~ao Flex\' de Documentos (The Unsupervised Dimensionality Reduction Weight on Flexible Document Organization)[In Portuguese]—0
InfoClus: Informative Clustering of High-dimensional Data Embeddings—0
Information loss from dimensionality reduction in 5D-Gaussian spectral data—0
Information Processing by Neuron Populations in the Central Nervous System: Mathematical Structure of Data and Operations—0
Information retrieval in single cell chromatin analysis using TF-IDF transformation methods—0
Information-Theoretic Bounds and Approximations in Neural Population Coding—0
Information Theoretic Feature Transformation Learning for Brain Interfaces—0
Information-Theoretic Representation Learning for Positive-Unlabeled Classification—0
Feature Clustering for Support Identification in Extreme Regions—0
Infrastructure-Assisted Collaborative Perception in Automated Valet Parking: A Safety Perspective—0
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
Input Guided Multiple Deconstruction Single Reconstruction neural network models for Matrix Factorization—0
In search of the most efficient and memory-saving visualization of high dimensional data—0
An Empirical Study on Fault Detection and Root Cause Analysis of Indium Tin Oxide Electrodes by Processing S-parameter Patterns—0
InstanceBEV: Unifying Instance and BEV Representation for Global Modeling—0
Instance Space Analysis for the Car Sequencing Problem—0
Instance-wise Linearization of Neural Network for Model Interpretation—0
Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks—0
Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks—0
Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data—0
Integrative analysis reveals disrupted pathways regulated by microRNAs in cancer—0
Adaptive Weighted Multi-View Clustering—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