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 1876–1900 of 3304 papers

TitleStatusHype
Functorial Manifold Learning—0
Fusing Vector Space Models for Domain-Specific Applications—0
Fusion of heterogeneous bands and kernels in hyperspectral image processing—0
Fusion of PCA and Segmented-PCA Domain Multiscale 2-D-SSA for Effective Spectral-Spatial Feature Extraction and Data Classification in Hyperspectral Imagery—0
Fuzzy Pooling—0
Fuzzy Pooling—0
GAD: A Real-time Gait Anomaly Detection System with Online Adaptive Learning—0
Game Theory Meets Statistical Mechanics in Deep Learning Design—0
GAN-EM: GAN based EM learning framework—0
GANs and Closures: Micro-Macro Consistency in Multiscale Modeling—0
GARA: A novel approach to Improve Genetic Algorithms' Accuracy and Efficiency by Utilizing Relationships among Genes—0
Gauge-optimal approximate learning for small data classification problems—0
Gaussian Determinantal Processes: a new model for directionality in data—0
Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection—0
Gaussian Mixture Models with Component Means Constrained in Pre-selected Subspaces—0
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Gaze-Sensing LEDs for Head Mounted Displays—0
Gene Expression based Survival Prediction for Cancer Patients: A Topic Modeling Approach—0
Bayesian Inference over the Stiefel Manifold via the Givens Representation—0
Generalisation and Sharing in Triplet Convnets for Sketch based Visual Search—0
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference—0
Generalizable Spectral Embedding with an Application to UMAP—0
Generalization Ability Analysis of Through-the-Wall Radar Human Activity Recognition—0
Generalized BackPropagation, Étude De Cas: Orthogonality—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