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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 3151–3175 of 3304 papers

TitleStatusHype
Neural Codes for Image RetrievalCode0
A Systematic Study of Semantic Vector Space Model Parameters—0
A Knowledge-based Representation for Cross-Language Document Retrieval and Categorization—0
Word Embeddings through Hellinger PCA—0
Approximate Matrix Multiplication with Application to Linear Embeddings—0
Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut—0
An Efficient Feature Selection in Classification of Audio Files—0
SRA: Fast Removal of General Multipath for ToF Sensors—0
A survey of dimensionality reduction techniques—0
Combination of PCA with SMOTE Resampling to Boost the Prediction Rate in Lung Cancer Dataset—0
Dimensionality reduction with subgaussian matrices: a unified theory—0
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable ModelsCode0
Applying Supervised Learning Algorithms and a New Feature Selection Method to Predict Coronary Artery Disease—0
Human Activity Recognition using Smartphone—0
A Bilinear Programming Approach for Multiagent Planning—0
Highly comparative feature-based time-series classification—0
Factorized Point Process Intensities: A Spatial Analysis of Professional BasketballCode0
A Large Scale Evaluation of Distributional Semantic Models: Parameters, Interactions and Model Selection—0
Learning Deep Representations By Distributed Random Samplings—0
Sparse Matrix-based Random Projection for Classification—0
Face Recognition using Hough Peaks extracted from the significant blocks of the Gradient Image—0
An adaptive block based integrated LDP,GLCM,and Morphological features for Face Recognition—0
Robust Transfer Principal Component Analysis with Rank Constraints—0
Compressive Feature Learning—0
Robust learning of low-dimensional dynamics from large neural ensembles—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