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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 3101–3150 of 3304 papers

TitleStatusHype
基於稀疏表示之語者識別 (Sparse Representation Based Speaker Identification) [In Chinese]—0
Random Manhattan Integer Indexing: Incremental L1 Normed Vector Space Construction—0
Unsupervised Bump Hunting Using Principal Components—0
A Deep Graph Embedding Network Model for Face Recognition—0
A theoretical contribution to the fast implementation of null linear discriminant analysis method using random matrix multiplication with scatter matrices—0
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models—0
Statistical and computational trade-offs in estimation of sparse principal components—0
Diffusion Fingerprints—0
Dimensionality Reduction of Affine Variational Inequalities Using Random Projections—0
Down-Sampling coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
Linear Contour Learning: A Method for Supervised Dimension Reduction—0
Mixed-Variate Restricted Boltzmann Machines—0
A Flexible Iterative Framework for Consensus Clustering—0
Multilayer bootstrap networks—0
TCDSCSS: Dimensionality Reduction to Evaluate Texts of Varying Lengths - an IR Approach—0
Contrasting Syntagmatic and Paradigmatic Relations: Insights from Distributional Semantic Models—0
Distributional Semantics in R with the wordspace Package—0
Identifying semantic relations in a specialized corpus through distributional analysis of a cooccurrence tensor—0
Hyperspectral Imaging and Analysis for Sparse Reconstruction and Recognition—0
Enhancing the Accuracy of Biometric Feature Extraction Fusion Using Gabor Filter and Mahalanobis Distance Algorithm—0
A feature construction framework based on outlier detection and discriminative pattern mining—0
From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices—0
Cortical spatio-temporal dimensionality reduction for visual grouping—0
Extracting lexico-semantic relations from specialized corpora using a word space model (Analyse distributionnelle de corpus sp\'ecialis\'es pour l'identification de relations lexico-s\'emantiques) [in French]—0
Image patch analysis and clustering of sunspots: a dimensionality reduction approach—0
Factors of Transferability for a Generic ConvNet Representation—0
Convex Optimization Learning of Faithful Euclidean Distance Representations in Nonlinear Dimensionality Reduction—0
Caffe: Convolutional Architecture for Fast Feature EmbeddingCode0
Dimensionality reduction for time series data—0
Unsupervised Deep Haar Scattering on Graphs—0
Linear Dimensionality Reduction: Survey, Insights, and GeneralizationsCode0
The constitution of visual perceptual units in the functional architecture of V1—0
Automated Disease Normalization with Low Rank Approximations—0
Merging SVMs with Linear Discriminant Analysis: A Combined Model—0
Transfer Joint Matching for Unsupervised Domain Adaptation—0
A Compact and Discriminative Face Track Descriptor—0
Grassmann Averages for Scalable Robust PCA—0
Hierarchical Feature Hashing for Fast Dimensionality Reduction—0
Compact Representation for Image Classification: To Choose or to Compress?—0
Interpretable Semantic Vectors from a Joint Model of Brain- and Text- Based Meaning—0
Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors—0
The role of dimensionality reduction in linear classification—0
Kronecker PCA Based Spatio-Temporal Modeling of Video for Dismount Classification—0
Comparing Similarity Measures for Distributional Thesauri—0
The AV-LASYN Database : A synchronous corpus of audio and 3D facial marker data for audio-visual laughter synthesis—0
How to Use less Features and Reach Better Performance in Author Gender Identification—0
Randomized Sketches of Convex Programs with Sharp Guarantees—0
Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization—0
Subspace clustering of dimensionality-reduced data—0
Tight bounds for learning a mixture of two gaussians—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