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 1201–1250 of 3304 papers

TitleStatusHype
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces—0
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity—0
Impact of the composition of feature extraction and class sampling in medicare fraud detection—0
Finding Rule-Interpretable Non-Negative Data Representation—0
Augmentation Component Analysis: Modeling Similarity via the Augmentation OverlapsCode0
AVIDA: Alternating method for Visualizing and Integrating Data—0
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time SeriesCode1
Features extraction and reduction techniques with optimized SVM for Persian/Arabic handwritten digits recognitionCode0
Principal Component Analysis based frameworks for efficient missing data imputation algorithms—0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
Cost-efficient Gaussian Tensor Network Embeddings for Tensor-structured Inputs—0
Trainable Weight Averaging: A General Approach for Subspace TrainingCode1
ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNECode0
PCA-Boosted Autoencoders for Nonlinear Dimensionality Reduction in Low Data Regimes—0
The Forecasting performance of the Factor model with Martingale Difference errors—0
Spatial Transcriptomics Dimensionality Reduction using Wavelet BasesCode0
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series ForecastingCode1
High-dimensional additive Gaussian processes under monotonicity constraintsCode1
DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with TreemapsCode1
DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterpartsCode1
Dimension Reduction for Efficient Dense Retrieval via Conditional AutoencoderCode1
Precoder Design for Correlated Data Aggregation via Over-the-Air Computation in Sensor Networks—0
Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction ModelsCode1
Revisiting Classical Multiclass Linear Discriminant Analysis with a Novel Prototype-based Interpretable Solution—0
LIDER: An Efficient High-dimensional Learned Index for Large-scale Dense Passage Retrieval—0
A New Dimensionality Reduction Method Based on Hensel's Compression for Privacy Protection in Federated Learning—0
Uniform Manifold Approximation with Two-phase OptimizationCode1
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning—0
Drone Flocking Optimization using NSGA-II and Principal Component Analysis—0
Novel optimized crow search algorithm for feature selectionCode0
Learning Effective SDEs from Brownian Dynamics Simulations of Colloidal ParticlesCode0
Local Explanation of Dimensionality ReductionCode0
Representative period selection for power system planning using autoencoder-based dimensionality reduction—0
BYTECOVER2: TOWARDS DIMENSIONALITY REDUCTION OF LATENT EMBEDDING FOR EFFICIENT COVER SONG IDENTIFICATION—0
On the Use of Dimension Reduction or Signal Separation Methods for Nitrogen River Pollution Source Identification—0
Trainable Compound Activation Functions for Machine Learning—0
Dimension Reduction for time series with Variational AutoEncoders—0
Spherical Rotation Dimension Reduction with Geometric Loss FunctionsCode0
Capturing the Denoising Effect of PCA via Compression Ratio—0
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations—0
Exploring Dimensionality Reduction Techniques in Multilingual Transformers—0
A dynamical systems based framework for dimension reduction—0
Diagnosing and Fixing Manifold Overfitting in Deep Generative ModelsCode1
Wassmap: Wasserstein Isometric Mapping for Image Manifold LearningCode0
DMCNet: Diversified Model Combination Network for Understanding Engagement from Video ScreengrabsCode1
Assessment of convolutional recurrent autoencoder network for learning wave propagation—0
T- Hop: Tensor representation of paths in graph convolutional networks—0
RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification—0
Weight Matrix Dimensionality Reduction in Deep Learning via Kronecker Multi-layer ArchitecturesCode0
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems—0
Show:102550
← PrevPage 25 of 67Next →
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