Cost functions for pairwise data clustering
Copyright policy )Cost functions for pairwise data clustering
Cost functions for non-hierarchical pairwise clustering are introduced, in the probabilistic autoencoder framework, by the request of maximal average similarity between the input and the output of the autoencoder. The partition provided by these cost functions identifies clusters with dense connected regions in data space; differences and similarities with respect to a well known cost function for pairwise clustering are outlined.
5 pages, 4 figures
Data structures, probabilisit autoencoder framework, Computational problems in statistics, maximal average similarity, FOS: Physical sciences, Potts spin Hamiltonian ground state, Quantum equilibrium statistical mechanics (general), Disordered Systems and Neural Networks (cond-mat.dis-nn), Condensed Matter - Disordered Systems and Neural Networks, Lattice systems (Ising, dimer, Potts, etc.) and systems on graphs arising in equilibrium statistical mechanics
Data structures, probabilisit autoencoder framework, Computational problems in statistics, maximal average similarity, FOS: Physical sciences, Potts spin Hamiltonian ground state, Quantum equilibrium statistical mechanics (general), Disordered Systems and Neural Networks (cond-mat.dis-nn), Condensed Matter - Disordered Systems and Neural Networks, Lattice systems (Ising, dimer, Potts, etc.) and systems on graphs arising in equilibrium statistical mechanics
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