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 671–680 of 3304 papers

TitleStatusHype
Linear normalised hash function for clustering gene sequences and identifying reference sequences from multiple sequence alignments—0
Metric Space Magnitude for Evaluating the Diversity of Latent RepresentationsCode1
A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography—0
Learning Arousal-Valence Representation from Categorical Emotion Labels of SpeechCode1
Unsupervised Learning for Topological Classification of Transportation Networks—0
Detection and Identification Accuracy of PCA-Accelerated Real-Time Processing of Hyperspectral Imagery—0
Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation—0
Applying Dimensionality Reduction as Precursor to LSTM-CNN Models for Classifying Imagery and Motor Signals in ECoG-Based BCIsCode0
Thinking Outside the Box: Orthogonal Approach to Equalizing Protected Attributes—0
ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches—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