SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 2001–2050 of 3304 papers

TitleStatusHype
Coupled Control Systems: Periodic Orbit Generation with Application to Quadrupedal Locomotion—0
Gaze-Sensing LEDs for Head Mounted Displays—0
Unsupervised machine learning of quantum phase transitions using diffusion maps—0
Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters—0
Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO—0
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier DetectionCode1
Multivariate Functional Regression via Nested Reduced-Rank Regularization—0
Multi-Scale Superpatch Matching using Dual Superpixel Descriptors—0
Supervised Domain Adaptation using Graph EmbeddingCode1
Xtreaming: an incremental multidimensional projection technique and its application to streaming data—0
Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks—0
BasisVAE: Translation-invariant feature-level clustering with Variational AutoencodersCode1
Spherical Principal Curves—0
Graphon Pooling in Graph Neural Networks—0
Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection SystemCode0
Dimensionality reduction to maximize prediction generalization capabilityCode1
Statistical power for cluster analysisCode1
Supervised Dimensionality Reduction and Visualization using Centroid-encoder—0
The Effectiveness of Johnson-Lindenstrauss Transform for High Dimensional Optimization With Adversarial Outliers, and the Recovery—0
High-Dimensional Feature Selection for Genomic DatasetsCode0
Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence—0
Dimensionality Reduction of Movement Primitives in Parameter Space—0
Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras—0
Multivariate time-series modeling with generative neural networks—0
Reliable Distributed Clustering with Redundant Data Assignment—0
Dimensionality Reduction and Motion Clustering during Activities of Daily Living: 3, 4, and 7 Degree-of-Freedom Arm Movements—0
t-viSNE: Interactive Assessment and Interpretation of t-SNE ProjectionsCode1
Fair Principal Component Analysis and Filter Design—0
Deep reconstruction of strange attractors from time seriesCode1
Stable Sparse Subspace Embedding for Dimensionality Reduction—0
Molecular Insights from Conformational Ensembles via Machine LearningCode1
Optimal Iterative Sketching with the Subsampled Randomized Hadamard Transform—0
NCVis: Noise Contrastive Approach for Scalable VisualizationCode1
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio—0
Optimal estimation of sparse topic models—0
ProjectionPathExplorer: Exploring Visual Patterns in Projected Decision-Making PathsCode0
Neighborhood Structure Assisted Non-negative Matrix Factorization and its Application in Unsupervised Point-wise Anomaly Detection—0
ShapeVis: High-dimensional Data Visualization at Scale—0
Unifying Deep Local and Global Features for Image SearchCode1
Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality ReductionCode0
A Correspondence Analysis Framework for Author-Conference Recommendations—0
A kernel Principal Component Analysis (kPCA) digest with a new backward mapping (pre-image reconstruction) strategy—0
Review of Single-cell RNA-seq Data Clustering for Cell Type Identification and Characterization—0
Upper bounds for Model-Free Row-Sparse Principal Component Analysis—0
Estimating Model Uncertainty of Neural Network in Sparse Information Form—0
MODiR: Multi-Objective Dimensionality Reduction for Joint Data Visualisation—0
Measuring group-separability in geometrical space for evaluation of pattern recognition and embedding algorithms—0
Interpreting LSTM Prediction on Solar Flare Eruption with Time-series ClusteringCode0
Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational AutoencodersCode0
Deep learning to discover and predict dynamics on an inertial manifoldCode0
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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