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Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 1326–1350 of 3304 papers

TitleStatusHype
A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images—0
Computing Approximate _p Sensitivities—0
A Novel method for Schizophrenia classification using nonlinear features and neural networks—0
Fitting a Simplicial Complex using a Variation of k-means—0
Flashlight Search Medial Axis: A Pixel-Free Pore-Network Extraction Algorithm—0
Bayesian Inference over the Stiefel Manifold via the Givens Representation—0
Flexible sampling of discrete data correlations without the marginal distributions—0
Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey—0
Forecastable Component Analysis (ForeCA)—0
Forecasting Nonnegative Time Series via Sliding Mask Method (SMM) and Latent Clustered Forecast (LCF)—0
Forensic Analysis of Video Files Using Metadata—0
Formation-Controlled Dimensionality Reduction—0
Forward-Cooperation-Backward (FCB) learning in a Multi-Encoding Uni-Decoding neural network architecture—0
Efficiently Computing Similarities to Private Datasets—0
Foundations of Coupled Nonlinear Dimensionality Reduction—0
Fourier analysis perspective for sufficient dimension reduction problem—0
Fourier-Invertible Neural Encoder (FINE) for Homogeneous Flows—0
Fractal Descriptors of Texture Images Based on the Triangular Prism Dimension—0
FRANS: Automatic Feature Extraction for Time Series Forecasting—0
FRE: A Fast Method For Anomaly Detection And Segmentation—0
Fréchet Cumulative Covariance Net for Deep Nonlinear Sufficient Dimension Reduction with Random Objects—0
Free Lunch for Gait Recognition: A Novel Relation Descriptor—0
Frequency-dependent covariance reveals critical spatio-temporal patterns of synchronized activity in the human brain—0
Efficient Learning and Planning with Compressed Predictive States—0
Bridging Fairness and Environmental Sustainability in Natural Language Processing—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