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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2726–2750 of 3304 papers

TitleStatusHype
How Do People Differ? A Social Media Approach—0
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication—0
A Latent Variable Model for Two-Dimensional Canonical Correlation Analysis and its Variational Inference—0
How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices?—0
Parameter Free Hierarchical Graph-Based Clustering for Analyzing Continuous Word Embeddings—0
Jmp8 at SemEval-2017 Task 2: A simple and general distributional approach to estimate word similarityCode0
Joint Dimensionality Reduction and Metric Learning: A Geometric Take—0
Evaluating Feature Extraction Methods for Knowledge-based Biomedical Word Sense Disambiguation—0
Topology Analysis of International Networks Based on Debates in the United Nations—0
Sparse Deep Nonnegative Matrix Factorization—0
A Harmony Search Based Wrapper Feature Selection Method for Holistic Bangla word Recognition—0
A Survey on Multi-Task LearningCode0
SAR Target Recognition Using the Multi-aspect-aware Bidirectional LSTM Recurrent Neural Networks—0
Application of Dictionary Learning in Alleviating Computational Burden of EEG Source Localization—0
Using PCA and Factor Analysis for Dimensionality Reduction of Bio-informatics Data—0
Sketched Subspace Clustering—0
A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics—0
Structural Learning and Integrative Decomposition of Multi-View Data—0
Discriminative convolutional Fisher vector network for action recognition—0
Algorithms for Approximate Subtropical Matrix Factorization—0
Prediction with Dimension Reduction of Multiple Molecular Data Sources for Patient Survival—0
Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image RetrievalCode0
Predictive Liability Models and Visualizations of High Dimensional Retail Employee Data—0
Accelerated Stochastic Power IterationCode0
High-Performance FPGA Implementation of Equivariant Adaptive Separation via Independence Algorithm for Independent Component Analysis—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