SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities
We propose a framework to model the distribution of sequential data coming from a set of entities connected in a graph with a known topology. The method is based on a mixture of shared hidden Markov models (HMMs), which are trained in order to exploit the knowledge of the graph structure and in such a way that the obtained mixtures tend to be sparse. Experiments in different application domains demonstrate the effectiveness and versatility of the method.
 This paper is not very well written. I cannot find what is the main contribution of this paper. For example, equation (1) seems out of the scope of the main discussion of this paper. It does not look a big deal employing EM algorithm for estimating the HMM parameters, but it seems the main technical contribution of the paper.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities
We propose a framework to model the distribution of sequential data coming from a set of entities connected in a graph with a known topology. The method is based on a mixture of shared hidden Markov models (HMMs), which are trained in order to exploit the knowledge of the graph structure and in such a way that the obtained mixtures tend to be sparse. Experiments in different application domains demonstrate the effectiveness and versatility of the method.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper is not very well written. I cannot find what is the main contribution of this paper. For example, equation (1) seems out of the scope of the main discussion of this paper. It does not look a big deal employing EM algorithm for estimating the HMM parameters, but it seems the main technical contribution of the paper.
assistant
 This paper is not very well written. I cannot find what is the main contribution of this paper. For example, equation (1) seems out of the scope of the main discussion of this paper. It does not look a big deal employing EM algorithm for estimating the HMM parameters, but it seems the main technical contribution of the paper.