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 1026–1050 of 3304 papers

TitleStatusHype
Angular Embedding: A New Angular Robust Principal Component Analysis—0
Efficient Optimization for Discriminative Latent Class Models—0
Circuit design in biology and machine learning. II. Anomaly detection—0
Chiron: A Robust Recommendation System with Graph Regularizer—0
Angle constrained path to cluster multiple manifolds—0
Chasing Collective Variables using Autoencoders and biased trajectories—0
An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm—0
A Comprehensive Filter Feature Selection for Improving Document Classification—0
Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching—0
Channel Charting: Locating Users within the Radio Environment using Channel State Information—0
Efficient Large-Scale Urban Parking Prediction: Graph Coarsening Based on Real-Time Parking Service Capability—0
Efficient Learning and Planning with Compressed Predictive States—0
Efficient L1-Norm Principal-Component Analysis via Bit Flipping—0
A New Spatio-Spectral Morphological Segmentation For Multi-Spectral Remote-Sensing Images—0
Efficient Large-Scale Similarity Search Using Matrix Factorization—0
Efficiently Computing Similarities to Private Datasets—0
A new set of cluster driven composite development indicators—0
Cell2State: Learning Cell State Representations From Barcoded Single-Cell Gene-Expression Transitions—0
A new parsimonious method for classifying Cancer Tissue-of-Origin Based on DNA Methylation 450K data—0
Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects—0
A Comparison Study on Nonlinear Dimension Reduction Methods with Kernel Variations: Visualization, Optimization and Classification—0
Efficient GPU implementation of randomized SVD and its applications—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
Efficient Quantum Convolutional Neural Networks for Image Classification: Overcoming Hardware Constraints—0
EigenGS Representation: From Eigenspace to Gaussian Image Space—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