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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2901–2925 of 3304 papers

TitleStatusHype
Text comparison using word vector representations and dimensionality reduction—0
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series—0
Manifold Approximation by Moving Least-Squares Projection (MMLS)—0
Continuum directions for supervised dimension reduction—0
An Efficient Large-scale Semi-supervised Multi-label Classifier Capable of Handling Missing labels—0
Bayesian Inference on Matrix Manifolds for Linear Dimensionality Reduction—0
A New Approach to Dimensionality Reduction for Anomaly Detection in Data Traffic—0
Unsupervised Non Linear Dimensionality Reduction Machine Learning methods applied to Multiparametric MRI in cerebral ischemia: Preliminary Results—0
Discovery of Latent Factors in High-dimensional Data Using Tensor Methods—0
Sifting Common Information from Many VariablesCode0
A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets—0
Property-driven State-Space Coarsening for Continuous Time Markov Chains—0
Sequential Principal Curves Analysis—0
Unifying Geometric Features and Facial Action Units for Improved Performance of Facial Expression Analysis—0
Trace Quotient Meets Sparsity: A Method for Learning Low Dimensional Image Representations—0
Efficient Large-Scale Similarity Search Using Matrix Factorization—0
Decoding Emotional Experience through Physiological Signal Processing—0
Mixtape Application: Music Map Methodology and Evaluation—0
Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering—0
Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods—0
Subspace Perspective on Canonical Correlation Analysis: Dimension Reduction and Minimax Rates—0
Theano: A Python framework for fast computation of mathematical expressionsCode0
A Comparative Study of Text Preprocessing Approaches for Topic Detection of User Utterances—0
Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow—0
Random Projection Estimation of Discrete-Choice Models with Large Choice Sets—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