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 2101–2125 of 3304 papers

TitleStatusHype
Specific language impairment (SLI) detection pipeline from transcriptions of spontaneous narratives—0
Spectral convergence of diffusion maps: improved error bounds and an alternative normalisation—0
Spectral Convergence of the connection Laplacian from random samples—0
Spectral Diffusion Processes—0
Spectral Echolocation via the Wave Embedding—0
Spectral estimation from simulations via sketching—0
Spectral feature scaling method for supervised dimensionality reduction—0
Spectral independent component analysis with noise modeling for M/EEG source separation—0
Spectral Learning on Matrices and Tensors—0
Spectrally-Corrected and Regularized Linear Discriminant Analysis for Spiked Covariance Model—0
Spectral Non-Convex Optimization for Dimension Reduction with Hilbert-Schmidt Independence Criterion—0
Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey—0
Spectral Representations for Convolutional Neural Networks—0
Spectral Self-supervised Feature Selection—0
Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut—0
Speech Emotion Recognition Using Deep Sparse Auto-Encoder Extreme Learning Machine with a New Weighting Scheme and Spectro-Temporal Features Along with Classical Feature Selection and A New Quantum-Inspired Dimension Reduction Method—0
Spherical Principal Curves—0
Spike and slab Bayesian sparse principal component analysis—0
Spike and Slab Gaussian Process Latent Variable Models—0
SPreV—0
SRA: Fast Removal of General Multipath for ToF Sensors—0
SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps—0
SSBNet: Improving Visual Recognition Efficiency by Adaptive Sampling—0
Stabilization Analysis and Mode Recognition of Kerosene Supersonic Combustion: A Deep Learning Approach Based on Res-CNN-beta-VAE—0
Stable Recovery Of Sparse Vectors From Random Sinusoidal Feature Maps—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