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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2376–2400 of 3304 papers

TitleStatusHype
A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data—0
Image retrieval method based on CNN and dimension reduction—0
A witness function based construction of discriminative models using Hermite polynomials—0
Transfer Representation Learning with TSK Fuzzy System—0
FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals—0
Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction—0
Performance prediction of data streams on high-performance architecture—0
Stochastic Approximation Algorithms for Principal Component Analysis—0
Auto-weighted Mutli-view Sparse Reconstructive Embedding—0
Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections—0
Active Learning with TensorBoard Projector—0
Trigonometric comparison measure: A feature selection method for text categorization—0
Supervised Multiscale Dimension Reduction for Spatial Interaction Networks—0
Exact Cluster Recovery via Classical Multidimensional Scaling—0
Determining Principal Component Cardinality through the Principle of Minimum Description Length—0
Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery—0
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization—0
Group Preserving Label Embedding for Multi-Label Classification—0
bigMap: Big Data Mapping with Parallelized t-SNE—0
A determinantal point process for column subset selection—0
Random Projection in Deep Neural NetworksCode0
Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning—0
Detecting the Trend in Musical Taste over the Decade -- A Novel Feature Extraction Algorithm to Classify Musical Content with Simple Features—0
Nonlinear demixed component analysis for neural population data as a low-rank kernel regression problemCode0
Deep Variational Sufficient Dimensionality 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