SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 3231–3240 of 3304 papers

TitleStatusHype
Dopamine Transporter SPECT Image Classification for Neurodegenerative Parkinsonism via Diffusion Maps and Machine Learning Classifiers—0
Doubly Non-Central Beta Matrix Factorization for Stable Dimensionality Reduction of Bounded Support Matrix Data—0
Down-Sampling coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
DPCA: Dimensionality Reduction for Discriminative Analytics of Multiple Large-Scale Datasets—0
DPDR: A novel machine learning method for the Decision Process for Dimensionality Reduction—0
Drone Flocking Optimization using NSGA-II and Principal Component Analysis—0
Dual-band feature selection for maturity classification of specialty crops by hyperspectral imaging—0
DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality—0
Dynamical Component Analysis (DyCA): Dimensionality Reduction For High-Dimensional Deterministic Time-Series—0
Dynamical Mode Recognition of Coupled Flame Oscillators by Supervised and Unsupervised Learning Approaches—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