SOTAVerified

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 1676–1700 of 3304 papers

TitleStatusHype
Structured Sparse Non-negative Matrix Factorization with L20-Norm for scRNA-seq Data AnalysisCode0
Efficient channel charting via phase-insensitive distance computation—0
Chasing Collective Variables using Autoencoders and biased trajectories—0
On Geodesic Distances and Contextual Embedding Compression for Text ClassificationCode0
Towards Exploratory Landscape Analysis for Large-scale Optimization: A Dimensionality Reduction FrameworkCode0
Compact and Effective Representations for Sketch-based Image Retrieval—0
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopyCode0
Multidimensional Scaling for Gene Sequence Data with Autoencoders—0
Quantum algorithms for SVD-based data representation and analysisCode0
Grassmann Iterative Linear Discriminant Analysis with Proxy Matrix Optimization—0
Capturing patterns of variation unique to a specific datasetCode0
Jointly Modeling and Clustering Tensors in High Dimensions—0
Classifying herbal medicine origins by temporal and spectral data mining of electronic noseCode0
Unsupervised low-rank representations for speech emotion recognition—0
Revisiting Bayesian Autoencoders with MCMCCode0
Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap—0
Adversarially-Trained Nonnegative Matrix FactorizationCode0
Granger Causality Based Hierarchical Time Series Clustering for State Estimation—0
Class-Wise Principal Component Analysis for hyperspectral image feature extraction—0
Deep Features for training Support Vector Machine—0
Predicting Inflation with Recurrent Neural Networks—0
Adapting Speaker Embeddings for Speaker Diarisation—0
Dopamine Transporter SPECT Image Classification for Neurodegenerative Parkinsonism via Diffusion Maps and Machine Learning Classifiers—0
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression—0
Deep Learning of Conjugate MappingsCode0
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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