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 726–750 of 3304 papers

TitleStatusHype
Preventing Model Collapse in Gaussian Process Latent Variable ModelsCode0
DMSSN: Distilled Mixed Spectral-Spatial Network for Hyperspectral Salient Object DetectionCode0
Comparison of Methods in Skin Pigment Decomposition—0
Enhancing Dimension-Reduced Scatter Plots with Class and Feature CentroidsCode0
Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering—0
Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach—0
Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons—0
Representatividad Muestral en la Incertidumbre Simétrica Multivariada para la Selección de Atributos—0
Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation ModelsCode0
Uncovering Temporal Patterns in Visualizations of High-Dimensional Data—0
S+t-SNE -- Bringing Dimensionality Reduction to Data StreamsCode0
Assessing the similarity of real matrices with arbitrary shapeCode0
FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data—0
Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets—0
Infrastructure-Assisted Collaborative Perception in Automated Valet Parking: A Safety Perspective—0
A Wasserstein perspective of Vanilla GANs—0
Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems—0
Frequency-dependent covariance reveals critical spatio-temporal patterns of synchronized activity in the human brain—0
Text Clustering with Large Language Model Embeddings—0
Curvature Augmented Manifold Embedding and LearningCode0
Evaluating Unsupervised Dimensionality Reduction Methods for Pretrained Sentence Embeddings—0
Temporally-Consistent Koopman Autoencoders for Forecasting Dynamical Systems—0
Graph Regularized NMF with L20-norm for Unsupervised Feature Learning—0
Enhancing IoT Security Against DDoS Attacks through Federated Learning—0
Randomized Principal Component Analysis for Hyperspectral Image Classification—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