Online nonparametric Bayesian analysis of parsimonious Gaussian mixture models and scenes clustering

Abstract

The mixture model is a very powerful and flexible tool in clustering analysis. Based on the Dirichlet process and parsimonious Gaussian distribution, we propose a new nonparametric mixture framework for solving challenging clustering problems. Meanwhile, the inference of the model depends on the efficient online variational Bayesian approach, which enhances the information exchange between the whole and the part to a certain extent and applies to scalable datasets. The experiments on the scene database indicate that the novel clustering framework, when combined with a convolutional neural network for feature extraction, has meaningful advantages over other models.

Inspecting the Reliability of Geochemical Facies Identified for the Waterworks' Capture Zone in Germany

“…Finite normal mixture models as a model‐based clustering method with probabilistic outputs are the most common for the clustering of a wide range of datasets (e.g., Marbac et al 2017; Saranya et al 2020; Zhou and Wang 2020).…”

“…To address this knowledge gap, two algorithms previously not used for clustering a groundwater geochemical dataset were selected; Gaussian finite mixture modeling (GFMM) (Scrucca et al 2016) and spike‐and‐slab Bayesian model (SSB) (Partovi Nia and Davison 2012). Successful applications of GFMM (e.g., Ellefsen et al 2014; Ellefsen and Smith 2016; Scrucca 2016; Marbac et al 2017; Popp et al 2019; Saranya et al 2020; Zhou and Wang 2020), and SSB (e.g., Tadesse et al 2005; Partovi Nia 2009; Partovi Nia and Davison 2012; Anderson and Vehtari 2017; Canale et al 2017; Cao et al 2019; Bai et al 2021), have been widely documented for datasets other than those relating to groundwater geochemistry.…”

Research on path planning of autonomous vehicle based on RRT algorithm of Q-learning and obstacle distribution

“…Therefore, in this paper, we propose Gaussian distribution density points of obstacles to achieve dynamic step size. Gaussian distribution is widely used in data clustering in data science (Rapp et al, 2021;Zhou and Wang, 2021). By using the Gaussian distribution function to generate points concentrated around their mean values, as shown in Figure 3, the random points are uniformly distributed around the desired path to reduce the sampling space.…”