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Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1351–1375 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
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
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
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
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
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
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
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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