PCA-KL: a parametric dimensionality reduction approach for unsupervised metric learning

DOI10.1007/s11634-020-00434-3OpenAlexW3119265899MaRDI QIDQ2673341

Alexandre L. M. Levada

Publication date: 9 June 2022

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-020-00434-3

zbMATH Keywords

dimensionality reductionPCAKL-divergenceunsupervised metric learning

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Pattern recognition, speech recognition (68T10) Measures of information, entropy (94A17) Informational aspects of data analysis and big data (94A16)

Uses Software

• Silhouettes
• t-SNE

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On the equivalence between non-negative matrix factorization and probabilistic latent semantic indexing
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• Survey on distance metric learning and dimensionality reduction in data mining
• Principal component analysis.
• Modern multidimensional scaling. Theory and applications.
• Nearest Neighbour Searches and the Curse of Dimensionality
• 10.1162/153244304322972667
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• A survey on unsupervised outlier detection in high‐dimensional numerical data
• Metric Learning
• Learning the parts of objects by non-negative matrix factorization
• Nonlinear Dimensionality Reduction

This page was built for publication: PCA-KL: a parametric dimensionality reduction approach for unsupervised metric learning