SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

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Papers

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Showing 1351–1400 of 3304 papers

TitleStatusHype
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
Principal Component Analysis based frameworks for efficient missing data imputation algorithms—0
Features extraction and reduction techniques with optimized SVM for Persian/Arabic handwritten digits recognitionCode0
Cost-efficient Gaussian Tensor Network Embeddings for Tensor-structured Inputs—0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
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
Precoder Design for Correlated Data Aggregation via Over-the-Air Computation in Sensor Networks—0
LIDER: An Efficient High-dimensional Learned Index for Large-scale Dense Passage Retrieval—0
Revisiting Classical Multiclass Linear Discriminant Analysis with a Novel Prototype-based Interpretable Solution—0
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning—0
A New Dimensionality Reduction Method Based on Hensel's Compression for Privacy Protection in Federated Learning—0
Drone Flocking Optimization using NSGA-II and Principal Component Analysis—0
Learning Effective SDEs from Brownian Dynamics Simulations of Colloidal ParticlesCode0
Novel optimized crow search algorithm for feature selectionCode0
Local Explanation of Dimensionality ReductionCode0
Representative period selection for power system planning using autoencoder-based dimensionality reduction—0
On the Use of Dimension Reduction or Signal Separation Methods for Nitrogen River Pollution Source Identification—0
BYTECOVER2: TOWARDS DIMENSIONALITY REDUCTION OF LATENT EMBEDDING FOR EFFICIENT COVER SONG IDENTIFICATION—0
Trainable Compound Activation Functions for Machine Learning—0
Spherical Rotation Dimension Reduction with Geometric Loss FunctionsCode0
Dimension Reduction for time series with Variational AutoEncoders—0
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
Wassmap: Wasserstein Isometric Mapping for Image Manifold LearningCode0
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
Covariance matrix preparation for quantum principal component analysis—0
Knowledge Base Index Compression via Dimensionality and Precision ReductionCode0
Nearly minimax robust estimator of the mean vector by iterative spectral dimension reduction—0
An efficient real-time target tracking algorithm using adaptive feature fusion—0
MLPro: A System for Hosting Crowdsourced Machine Learning Challenges for Open-Ended Research Problems—0
The Fast Johnson-Lindenstrauss Transform is Even FasterCode0
Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting—0
Application of Dimensional Reduction in Artificial Neural Networks to Improve Emergency Department Triage During Chemical Mass Casualty Incidents—0
Ternary and Binary Quantization for Improved Classification—0
1-D CNN based Acoustic Scene Classification via Reducing Layer-wise Dimensionality—0
A distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentationCode0
Digital Fingerprinting of Microstructures—0
Theoretical Connection between Locally Linear Embedding, Factor Analysis, and Probabilistic PCA—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