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Model-based learning for point pattern data
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DOI：10.1016/j.patcog.2018.07.008.png)
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Abstract
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This article proposes a framework for model-based point pattern learning using point process theory. Likelihood functions for point pattern data derived from point process theory enable principled yet conceptually transparent extensions of learning tasks, such as classification, novelty detection and clustering, to point pattern data. Furthermore, tractable point pattern models as well as solutions for learning and decision making from point pattern data are developed. (C) 2018 The Authors. Published by Elsevier Ltd.
Keywords:
Point pattern
Point process
Random finite set
Multiple instance learning
Classification
Novelty detection
Clustering
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