Model-based classification and novelty detection for point pattern data

By B. Vo, Nhat-Quang Tran, Dinh Q. Phung. 2016

14 citations recorded by the source corpus.

Point patterns are sets or multi-sets of unordered elements that can be found in numerous data sources. However, in data analysis tasks such as classification and novelty detection, appropriate statistical models for point pattern data have not received much attention. This paper proposes the modelling of point pattern data via random finite sets (RFS). In particular, we propose appropriate likelihood functions, and a maximum likelihood estimator for learning a tractable family of RFS models. In novelty detection, we propose novel ranking functions based on RFS models, which substantially improve performance.

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Probabilistic & Bayesian Methods

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Citations in the corpus

DeepSetNet: Predicting Sets with Deep Neural Networks (2016, background): “U is the unit of hypervolume in R, which makes the joint distribution unitless [28, 45].”
Model-Based Multiple Instance Learning (2017, background): “These developments have been partially reported in [15], [16], [17], respectively.”
Learn to Predict Sets Using Feed-Forward Neural Networks (2020, background): “Approaches such as mixture models [31], [39], [40], learning distributions from a set of samples [41], [42], model-based multiple instance learning [43], and novelty detection from point pattern data [44], can be counted as few out many examples that use point patterns or sets as input or output and”
Joint Learning of Set Cardinality and State Distribution (2017, methodology): “Note that in contrast to previous works (Rezatoﬁghi et al. 2017; Vo et al. 2016; 2017) that assume that two sets of independent parameters (two independent networks) are required to represent the set distribution p ( Y|· ) , we will show that one set of parameters w is sufﬁcient to learn this…”