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 1051–1075 of 3304 papers

TitleStatusHype
Enhanced Neural Beamformer with Spatial Information for Target Speech Extraction—0
Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs—0
CAVIAR: Categorical-Variable Embeddings for Accurate and Robust Inference—0
A New Method for Performance Analysis in Nonlinear Dimensionality Reduction—0
A new method color MS-BSIF Features learning for the robust kinship verification—0
Causal learning with sufficient statistics: an information bottleneck approach—0
A feature construction framework based on outlier detection and discriminative pattern mining—0
ELA: Efficient Local Attention for Deep Convolutional Neural Networks—0
Causal Feature Selection with Dimension Reduction for Interpretable Text Classification—0
Causal Deep Learning—0
A new filter for dimensionality reduction and classification of hyperspectral images using GLCM features and mutual information—0
A New Dimensionality Reduction Method Based on Hensel's Compression for Privacy Protection in Federated Learning—0
A Comparison of Representation Learning Methods for Dimensionality Reduction of fMRI Scans for Classification of ADHD—0
Elastic deep autoencoder for text embedding clustering by an improved graph regularization—0
CASS: Cross Adversarial Source Separation via Autoencoder—0
A New Covariance Estimator for Sufficient Dimension Reduction in High-Dimensional and Undersized Sample Problems—0
CASE -- Condition-Aware Sentence Embeddings for Conditional Semantic Textual Similarity Measurement—0
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning—0
A fast, universal algorithm to learn parametric nonlinear embeddings—0
Cardiomyopathy Diagnosis Model from Endomyocardial Biopsy Specimens: Appropriate Feature Space and Class Boundary in Small Sample Size Data—0
Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting—0
A new band selection approach based on information theory and support vector machine for hyperspectral images reduction and classification—0
A New Approach to Dimensionality Reduction for Anomaly Detection in Data Traffic—0
Capture Agent Free Biosensing using Porous Silicon Arrays and Machine Learning—0
A Second-Order Majorant Algorithm for Nonnegative Matrix Factorization—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