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 2151–2200 of 3304 papers

TitleStatusHype
Structured Feature Selection—0
Study on Downlink CSI compression: Are Neural Networks the Only Solution?—0
Subject-independent Classification of Meditative State from the Resting State using EEG—0
Subspace approximation with outliers—0
Subspace clustering of dimensionality-reduced data—0
Subspace Embeddings for the Polynomial Kernel—0
Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection—0
Subspace Perspective on Canonical Correlation Analysis: Dimension Reduction and Minimax Rates—0
Sufficient Dimension Reduction for Average Causal Effect Estimation—0
0-dimensional Homology Preserving Dimensionality Reduction with TopoMap—0
Sufficient Dimension Reduction for High-Dimensional Regression and Low-Dimensional Embedding: Tutorial and Survey—0
Sufficient Forecasting Using Factor Models—0
Super-Bit Locality-Sensitive Hashing—0
Supervised classification methods applied to airborne hyperspectral images: Comparative study using mutual information—0
Supervised Dimensionality Reduction and Visualization using Centroid-encoder—0
Supervised Dimensionality Reduction via Distance Correlation Maximization—0
Supervised Exponential Family Principal Component Analysis via Convex Optimization—0
Supervised Kernel PCA For Longitudinal Data—0
Supervised Learning with First-to-Spike Decoding in Multilayer Spiking Neural Networks—0
Supervised Linear Dimension-Reduction Methods: Review, Extensions, and Comparisons—0
Supervised Manifold Learning via Random Forest Geometry-Preserving Proximities—0
Supervised Multiscale Dimension Reduction for Spatial Interaction Networks—0
Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction—0
Supervised Time Series Classification for Anomaly Detection in Subsea Engineering—0
Supervised Visualization for Data Exploration—0
On the Selection of Tuning Parameters for Patch-Stitching Embedding Methods—0
Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning—0
Surrogate to Poincaré inequalities on manifolds for dimension reduction in nonlinear feature spaces—0
Swin fMRI Transformer Predicts Early Neurodevelopmental Outcomes from Neonatal fMRI—0
Systematic analysis reveals key microRNAs as diagnostic and prognostic factors in progressive stages of lung cancer—0
System-reliability based multi-ensemble of GAN and one-class joint Gaussian distributions for unsupervised real-time structural health monitoring—0
Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration—0
TailorMe: Self-Supervised Learning of an Anatomically Constrained Volumetric Human Shape Model—0
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks—0
Taming Wild High Dimensional Text Data with a Fuzzy Lash—0
On Computational Modeling of Sleep-Wake Cycle—0
TCDSCSS: Dimensionality Reduction to Evaluate Texts of Varying Lengths - an IR Approach—0
TDAsweep: A Novel Dimensionality Reduction Method for Image Classification Tasks—0
TeamCEN at SemEval-2018 Task 1: Global Vectors Representation in Emotion Detection—0
Temporally-Consistent Koopman Autoencoders for Forecasting Dynamical Systems—0
TemporalPaD: a reinforcement-learning framework for temporal feature representation and dimension reduction—0
Temporal Streaming Batch Principal Component Analysis for Time Series Classification—0
Tensor-Based Classifiers for Hyperspectral Data Analysis—0
Tensor Decompositions: A New Concept in Brain Data Analysis?—0
Tensor Random Projection for Low Memory Dimension Reduction—0
Tensor Train Neighborhood Preserving Embedding—0
Tensor-Train Parameterization for Ultra Dimensionality Reduction—0
Tensor Train Random Projection—0
Ternary and Binary Quantization for Improved Classification—0
t-EVA: Time-Efficient t-SNE Video Annotation—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