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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 3251–3300 of 3304 papers

TitleStatusHype
SemEval-2012 Task 7: Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning—0
Variable noise and dimensionality reduction for sparse Gaussian processes—0
Modeling coherence in ESOL learner texts—0
Analyse en Composante Principale pour l'extraction des i-vecteurs en v\'erification du locuteur (Principal Component Analysis for i-vector extraction in speaker verification.) [in French]—0
Forecastable Component Analysis (ForeCA)—0
A hierarchical approach with feature selection for emotion recognition from speech—0
This also affects the context - Errors in extraction based summaries—0
Scikit-learn: Machine Learning in PythonCode0
Book Review: Graph-Based Natural Language Processing and Information Retrieval by Rada Mihalcea and Dragomir Radev—0
Dimensionality Reduction Using the Sparse Linear Model—0
Variational Gaussian Process Dynamical Systems—0
Demixed Principal Component Analysis—0
Maximum Covariance Unfolding : Manifold Learning for Bimodal Data—0
Sparse Manifold Clustering and Embedding—0
How to Evaluate Dimensionality Reduction? - Improving the Co-ranking MatrixCode0
Randomized Dimensionality Reduction for k-means Clustering—0
Kernel Methods for the Approximation of Nonlinear Systems—0
Face Recognition using Curvelet Transform—0
Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction—0
Vector Diffusion Maps and the Connection LaplacianCode0
Optimal learning rates for Kernel Conjugate Gradient regression—0
Random Projections for k-means Clustering—0
Unsupervised Kernel Dimension Reduction—0
Efficient Optimization for Discriminative Latent Class Models—0
Generative Local Metric Learning for Nearest Neighbor Classification—0
Penalized Principal Component Regression on Graphs for Analysis of Subnetworks—0
Worst-Case Linear Discriminant Analysis—0
Inductive Regularized Learning of Kernel Functions—0
Robust PCA via Outlier PursuitCode0
Human Face Recognition using Line Features—0
Quotient Based Multiresolution Image Fusion of Thermal and Visual Images Using Daubechies Wavelet Transform for Human Face Recognition—0
Multi-Label Prediction via Sparse Infinite CCA—0
Probabilistic Relational PCA—0
STDP enables spiking neurons to detect hidden causes of their inputs—0
Fast Graph Laplacian Regularized Kernel Learning via Semidefinite–Quadratic–Linear Programming—0
Sparse Metric Learning via Smooth Optimization—0
Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction—0
Hilbert space embeddings and metrics on probability measures—0
Feature Hashing for Large Scale Multitask Learning—0
Localized Sliced Inverse Regression—0
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity—0
Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method—0
Bayesian Exponential Family PCA—0
Dimensionality Reduction for Data in Multiple Feature Representations—0
Diffeomorphic Dimensionality Reduction—0
Supervised Exponential Family Principal Component Analysis via Convex Optimization—0
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification—0
Visualizing Data using t-SNE—0
3D Face Recognition with Sparse Spherical Representations—0
Locality and low-dimensions in the prediction of natural experience from fMRI—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