SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 671–680 of 3304 papers

TitleStatusHype
DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning—0
A case study : Influence of Dimension Reduction on regression trees-based Algorithms -Predicting Aeronautics Loads of a Derivative Aircraft—0
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information—0
Cone-Constrained Principal Component Analysis—0
Consistent Estimation of Low-Dimensional Latent Structure in High-Dimensional Data—0
Consistent Representation Learning for High Dimensional Data Analysis—0
A Powerful Face Preprocessing For Robust Kinship Verification based Tensor Analyses—0
Construction of neural networks for realization of localized deep learning—0
Content-Aware Tweet Location Inference using Quadtree Spatial Partitioning and Jaccard-Cosine Word Embedding—0
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—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