Similarity-Based Pattern Recognition

Third International Workshop, SIMBAD 2015, Copenhagen, Denmark, October 12-14, 2015. Proceedings

AvAasa Feragen,Marcello Pelillo

Häftad, Engelska, 2015

Del 9370 i serien Lecture Notes in Computer Science

532 kr

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Beskrivning

This book constitutes the proceedings of the Third International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD 2015, which was held in Copenahgen, Denmark, in October 2015.

Produktinformation

Utgivningsdatum:2015-10-16
Mått:155 x 235 x 14 mm
Vikt:371 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:229
Förlag:Springer International Publishing AG
ISBN:9783319242606

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Innehållsförteckning

A Novel Data Representation based on a Second-Order Dissimilarity Measure.- Characterizing Multiple Instance Datasets.- Supervised learning of diffiusion distance to improve histogram matching.- Similarity Analysis from Limiting Quantum Walks.- Introducing Negative Evidence in Ensemble Clustering.- Dissimilarity representations for low-resolution face recognition.- Deep metric learning using Triplet network.- Cluster Merging Based on Dominant Sets.- An Adaptive Radial Basis Function Kernel for Support Vector Data Description.- Robust initialization for learning Latent Dirichlet Allocation.- Unsupervised Motion Segmentation Using Metric Embedding of Features.- Transitive Assignment Kernels for Structural Classification.- Large scale Indefinite Kernel Fisher Discriminant.- Similarity-based User Identification across Social Networks.- Dominant-Set Clustering Using Multiple Affinity Matrices.- Distance-Based Network Recovery under Feature Correlation.- Discovery of salient low-dimensional dynamical structure using Hopfield Networks.- On Geodesic Exponential Kernels.- A Matrix Factorization Approach to Graph Compression.- A Geometrical Approach to Find Corresponding Patches in 3D Medical Surfaces.- Similarities, SDEs, and Most Probable Paths.- Can the optimum similarity matrix be selected before clustering for graph-based approaches?.- Approximate spectral clustering with utilized similarity information fusing geodesic based hybrid distance measures.

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