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Dimensionality Reduction

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

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Papers

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Showing 2401–2450 of 3304 papers

TitleStatusHype
GAN-EM: GAN based EM learning framework—0
Robust Subspace Approximation in a Stream—0
Model-based targeted dimensionality reduction for neuronal population data—0
Manifold Coordinates with Physical MeaningCode0
RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology—0
A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration—0
Detailed Investigation of Deep Features with Sparse Representation and Dimensionality Reduction in CBIR: A Comparative Study—0
Enhanced Expressive Power and Fast Training of Neural Networks by Random ProjectionsCode0
Global Sensitivity Analysis of High Dimensional Neuroscience Models: An Example of Neurovascular Coupling—0
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes—0
A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images—0
A case study : Influence of Dimension Reduction on regression trees-based Algorithms -Predicting Aeronautics Loads of a Derivative Aircraft—0
Subspace Clustering through Sub-ClustersCode0
Exploring the Deep Feature Space of a Cell Classification Neural Network—0
Unsupervised learning with contrastive latent variable modelsCode0
Interactive dimensionality reduction using similarity projections—0
Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoders—0
Matrix Product Operator Restricted Boltzmann Machines—0
Semi-supervised Deep Representation Learning for Multi-View Problems—0
Exploiting Capacity of Sewer System Using Unsupervised Learning Algorithms Combined with Dimensionality Reduction—0
Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extensions—0
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training—0
Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering—0
SRP: Efficient class-aware embedding learning for large-scale data via supervised random projectionsCode0
Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-End Speaker Verification—0
Representation Learning by Reconstructing Neighborhoods—0
Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis—0
Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds—0
The Price of Fair PCA: One Extra DimensionCode0
Non-linear Canonical Correlation Analysis: A Compressed Representation Approach—0
Contrastive Multivariate Singular Spectrum Analysis—0
Unsupervised Dimension Selection using a Blue Noise Spectrum—0
Alternating Diffusion Map Based Fusion of Multimodal Brain Connectivity Networks for IQ Prediction—0
A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation—0
Scaling Gaussian Process Regression with DerivativesCode0
Failing Loudly: An Empirical Study of Methods for Detecting Dataset ShiftCode0
Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large data sets—0
Perceptual Visual Interactive Learning—0
CatBoost: gradient boosting with categorical features supportCode1
Modified Multidimensional Scaling and High Dimensional Clustering—0
Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning—0
Optimal terminal dimensionality reduction in Euclidean space—0
Learning Inward Scaled Hypersphere Embedding: Exploring Projections in Higher Dimensions—0
Fast Randomized PCA for Sparse DataCode0
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable ModelsCode0
A Generative Model of Textures Using Hierarchical Probabilistic Principal Component Analysis—0
ABACUS: Unsupervised Multivariate Change Detection via Bayesian Source Separation—0
Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation ApproachCode0
Asymptotics for Sketching in Least Squares RegressionCode0
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring SignalsCode0
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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