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 2101–2150 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
Stable Sparse Subspace Embedding for Dimensionality Reduction—0
Static and Dynamic Robust PCA and Matrix Completion: A Review—0
Statistical Advantages of Oblique Randomized Decision Trees and Forests—0
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances—0
Statistical and computational trade-offs in estimation of sparse principal components—0
Statistical guarantees for generative models without domination—0
Statistical physics, Bayesian inference and neural information processing—0
Statistical tuning of artificial neural network—0
STDP enables spiking neurons to detect hidden causes of their inputs—0
Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction—0
Stochastic Approximation Algorithms for Principal Component Analysis—0
Stochastic Approximation for Online Tensorial Independent Component Analysis—0
Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction—0
Performance-Complexity Tradeoffs in Greedy Weak Submodular Maximization with Random Sampling—0
Stochastic Linear Bandits with Hidden Low Rank Structure—0
Stochastic Multidimensional Scaling—0
Stochastic Neighbor Embedding—0
Stochastic Neighbor Embedding separates well-separated clusters—0
Stochastic variance reduced multiplicative update for nonnegative matrix factorization—0
Stock Price Prediction using Principle Components—0
Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing—0
Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification—0
Strong and weak principles of neural dimension reduction—0
Structural Learning and Integrative Decomposition of Multi-View Data—0
Structured adaptive and random spinners for fast machine learning computations—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