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 551–575 of 3304 papers

TitleStatusHype
Understanding Clinical Decision-Making in Traditional East Asian Medicine through Dimensionality Reduction: An Empirical Investigation—0
Wireless Environment Information Sensing, Feature, Semantic, and Knowledge: Four Steps Towards 6G AI-Enabled Air Interface—0
Factors in Fashion: Factor Analysis towards the Mode—0
Sparse Modelling for Feature Learning in High Dimensional Data—0
Implementing NLPs in industrial process modeling: Addressing Categorical Variables—0
TemporalPaD: a reinforcement-learning framework for temporal feature representation and dimension reduction—0
Broadcast Product: Shape-aligned Element-wise Multiplication and Beyond—0
Exploring Semantic Clustering in Deep Reinforcement Learning for Video Games—0
Grading and Anomaly Detection for Automated Retinal Image Analysis using Deep Learning—0
Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate DataCode0
Stochastic neighborhood embedding and the gradient flow of relative entropy—0
Data-driven Probabilistic Trajectory Learning with High Temporal Resolution in Terminal Airspace—0
A parametric framework for kernel-based dynamic mode decomposition using deep learningCode0
Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotions—0
Self-Supervised Graph Embedding Clustering—0
Representation Loss Minimization with Randomized Selection Strategy for Efficient Environmental Fake Audio Detection—0
Statistical tuning of artificial neural network—0
FLeNS: Federated Learning with Enhanced Nesterov-Newton SketchCode0
Exploring Fine-grained Retail Product Discrimination with Zero-shot Object Classification Using Vision-Language ModelsCode0
Relevance-driven Decision Making for Safer and More Efficient Human Robot Collaboration—0
Shape-informed surrogate models based on signed distance function domain encoding—0
User-friendly Foundation Model Adapters for Multivariate Time Series Classification—0
The Impact of Element Ordering on LM Agent PerformanceCode0
Cost-informed dimensionality reduction for structural digital twin technologies—0
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction—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