SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1901–1925 of 3304 papers

TitleStatusHype
Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis—0
Reduced-Rank Multi-objective Policy Learning and Optimization—0
Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay—0
Reducing Redundancy in the Bottleneck Representation of the Autoencoders—0
Reducing training requirements through evolutionary based dimension reduction and subject transfer—0
Regression-aware decompositions—0
Regression of high dimensional angular momentum states of light—0
Regularisation for PCA- and SVD-type matrix factorisations—0
Regularized Pooling—0
Regular Variation in Hilbert Spaces and Principal Component Analysis for Functional Extremes—0
Rehabilitating Isomap: Euclidean Representation of Geodesic Structure—0
Rehabilitation of Count-based Models for Word Vector Representations—0
Improving Surrogate Model Robustness to Perturbations for Dynamical Systems Through Machine Learning and Data Assimilation—0
Relation between PLS and OLS regression in terms of the eigenvalue distribution of the regressor covariance matrix—0
Parameter-wise co-clustering for high-dimensional data—0
Relevance-driven Decision Making for Safer and More Efficient Human Robot Collaboration—0
Relevance for Human Robot Collaboration—0
Reliable Distributed Clustering with Redundant Data Assignment—0
Representational Analysis of Binding in Language Models—0
Representation-Constrained Autoencoders and an Application to Wireless Positioning—0
Representation Learning by Reconstructing Neighborhoods—0
Representation learning of dynamic networks—0
Representation Learning on Graphs: Methods and Applications—0
Representation Loss Minimization with Randomized Selection Strategy for Efficient Environmental Fake Audio Detection—0
Representation of big data by dimension reduction—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