A Latent-Variable Lattice Model
نویسنده
چکیده
Markov random field (MRF) learning is intractable, and the approximation algorithms are computationally expensive. Since only a small subset of MRF is used frequently in computer vision, we characterize this subset with three concepts: (1) Lattice, (2) Homogeneity, and (3) Inertia; and design a non-markov high-bias low-variance model as an alternative to this subclass of MRF. Our goal is robust learning from small datasets. Our learning algorithm uses vector quantization and, at time complexity O(T d logT ) for a hypercube of size T , is much faster than that of general-purpose MRF.
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