SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 371–380 of 3304 papers

TitleStatusHype
A Process for Topic Modelling Via Word Embeddings—0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—0
A quantitative fusion strategy of stock picking and timing based on Particle Swarm Optimized-Back Propagation Neural Network and Multivariate Gaussian-Hidden Markov Model—0
A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning—0
A Kernelization-Based Approach to Nonparametric Binary Choice Models—0
A Radiomics-Incorporated Deep Ensemble Learning Model for Multi-Parametric MRI-based Glioma Segmentation—0
A Reconfigurable Low Power High Throughput Architecture for Deep Network Training—0
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis—0
A Knowledge-based Representation for Cross-Language Document Retrieval and Categorization—0
A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images—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