SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2421–2430 of 3304 papers

TitleStatusHype
Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extensions—0
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training—0
Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering—0
SRP: Efficient class-aware embedding learning for large-scale data via supervised random projectionsCode0
Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-End Speaker Verification—0
Representation Learning by Reconstructing Neighborhoods—0
Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis—0
Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds—0
The Price of Fair PCA: One Extra DimensionCode0
Non-linear Canonical Correlation Analysis: A Compressed Representation Approach—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