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Infinite Latent Topic Models for Document Analysis
http://doi.org/10.5626/JOK.2018.45.7.701
Since the concept of the topic is highly abstract, the characterization of the topics of a text is not clearly defined. Depending on the problem’s context or needs, various levels of detail may be provided, which could make it difficult to automatically analyze documents. This paper presents infinite topic extensions to the well-known model of Latent Dirichlet Allocation (LDA) i.e., the infinite Latent Dirichlet Topic model and the infinite Latent Markov Topic model. The first model simply relaxes the constraint of fixed known number of topics in LDA using the method of the Dirichlet process. The second model further extends it by including Markov dynamics that captures the sequential evolution of topics in a text. Both models are theoretically rigorous and structurally flexible, as well as being capable of capturing document organizations at a desired level of topics. A set of experiments show interesting results and a more intuitive topic characterization and local stationarity properties than related models with Gibbs sampling and variational inferences.
Health State Clustering and Prediction Based on Bayesian HMM
http://doi.org/10.5626/JOK.2017.44.10.1026
In this paper a Bayesian modeling and duration-based prediction method is proposed for health clinic time series data using the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). HDP-HMM is a Bayesian extension of HMM which can find the optimal number of health states, a number which is highly uncertain and even difficult to estimate under the context of health dynamics. Test results of HDP-HMM using simulated data and real health clinic data have shown interesting modeling behaviors and promising prediction performance over the span of up to five years. The future of health change is uncertain and its prediction is inherently difficult, but experimental results on health clinic data suggests that practical long-term prediction is possible and can be made useful if we present multiple hypotheses given dynamic contexts as defined by HMM states.
Parallel Gaussian Processes for Gait and Phase Analysis
This paper proposes a sequential state estimation model consisting of continuous and discrete variables, as a way of generalizing all discrete-state factorial HMM, and gives a design of gait motion model based on the idea. The discrete state variable implements a Markov chain that models the gait dynamics, and for each state of the Markov chain, we created a Gaussian process over the space of the continuous variable. The Markov chain controls the switching among Gaussian processes, each of which models the rotation or various views of a gait state. Then a particle filter-based algorithm is presented to give an approximate filtering solution. Given an input vector sequence presented over time, this finds a trajectory that follows a Gaussian process and occasionally switches to another dynamically. Experimental results show that the proposed model can provide a very intuitive interpretation of video-based gait into a sequence of poses and a sequence of posture states.
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