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 2876–2900 of 3304 papers

TitleStatusHype
CAMEL: Curvature-Augmented Manifold Embedding and Learning—0
Can Genetic Programming Do Manifold Learning Too?—0
Canonical Correlation Analysis of Datasets with a Common Source Graph—0
Canonical Variates in Wasserstein Metric Space—0
Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?—0
Capacity Analysis of Vector Symbolic Architectures—0
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature—0
Capture Agent Free Biosensing using Porous Silicon Arrays and Machine Learning—0
Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting—0
Cardiomyopathy Diagnosis Model from Endomyocardial Biopsy Specimens: Appropriate Feature Space and Class Boundary in Small Sample Size Data—0
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning—0
CASE -- Condition-Aware Sentence Embeddings for Conditional Semantic Textual Similarity Measurement—0
CASS: Cross Adversarial Source Separation via Autoencoder—0
Causal Deep Learning—0
Causal Feature Selection with Dimension Reduction for Interpretable Text Classification—0
Causal learning with sufficient statistics: an information bottleneck approach—0
CAVIAR: Categorical-Variable Embeddings for Accurate and Robust Inference—0
Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects—0
Cell2State: Learning Cell State Representations From Barcoded Single-Cell Gene-Expression Transitions—0
Channel Charting: Locating Users within the Radio Environment using Channel State Information—0
Chasing Collective Variables using Autoencoders and biased trajectories—0
Chiron: A Robust Recommendation System with Graph Regularizer—0
Circuit design in biology and machine learning. II. Anomaly detection—0
Circular Coordinate Methods with Generalized Penalty Functions—0
CitiusNLP at SemEval-2018 Task 10: The Use of Transparent Distributional Models and Salient Contexts to Discriminate Word Attributes—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