SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 3151–3160 of 3304 papers

TitleStatusHype
Neural Codes for Image RetrievalCode0
Word Embeddings through Hellinger PCA—0
A Systematic Study of Semantic Vector Space Model Parameters—0
A Knowledge-based Representation for Cross-Language Document Retrieval and Categorization—0
Approximate Matrix Multiplication with Application to Linear Embeddings—0
Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut—0
An Efficient Feature Selection in Classification of Audio Files—0
SRA: Fast Removal of General Multipath for ToF Sensors—0
A survey of dimensionality reduction techniques—0
Combination of PCA with SMOTE Resampling to Boost the Prediction Rate in Lung Cancer Dataset—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