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Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2426–2450 of 3304 papers

TitleStatusHype
A case study : Influence of Dimension Reduction on regression trees-based Algorithms -Predicting Aeronautics Loads of a Derivative Aircraft—0
Exploring the Deep Feature Space of a Cell Classification Neural Network—0
Subspace Clustering through Sub-ClustersCode0
Unsupervised learning with contrastive latent variable modelsCode0
Interactive dimensionality reduction using similarity projections—0
Matrix Product Operator Restricted Boltzmann Machines—0
Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoders—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
Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering—0
Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extensions—0
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training—0
Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-End Speaker Verification—0
SRP: Efficient class-aware embedding learning for large-scale data via supervised random projectionsCode0
Representation Learning by Reconstructing Neighborhoods—0
Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis—0
The Price of Fair PCA: One Extra DimensionCode0
Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds—0
Unsupervised Dimension Selection using a Blue Noise Spectrum—0
Non-linear Canonical Correlation Analysis: A Compressed Representation Approach—0
Contrastive Multivariate Singular Spectrum Analysis—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
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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