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 1241–1250 of 3304 papers

TitleStatusHype
A kernel Principal Component Analysis (kPCA) digest with a new backward mapping (pre-image reconstruction) strategy—0
Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data—0
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems—0
A Radiomics-Incorporated Deep Ensemble Learning Model for Multi-Parametric MRI-based Glioma Segmentation—0
A Kernelization-Based Approach to Nonparametric Binary Choice Models—0
Adapting Speaker Embeddings for Speaker Diarisation—0
Decentralized Equalization for Massive MIMO Systems With Colored Noise Samples—0
Data-Space Inversion Using a Recurrent Autoencoder for Time-Series Parameterization—0
A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning—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
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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