Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Clustering is a fundamental task in many vision applications. To date, most clustering algorithms work in a batch setting and training examples must be gathered in a large group before learning can begin. Here  explore incremental clustering, in which data can arrive continuously.  present a novel incremental model-based clustering algorithm based on nonparametric Bayesian methods, which  call memory bounded variational Dirichlet process (MB-VDP). The number of clusters are determined flexibly by the data and the approach can be used to automatically discover object categories. The computational requirements required to produce model updates are bounded and do not grow with the amount of data processed. The technique is ll suited to very large datasets, and  show that our approach outperforms existing online alternatives for learning nonparametric Bayesian mixture models.
['Ryan Gomes', 'Max Welling', 'Pietro Perona']
I am writing a research paper on a idea Incremental learning of nonparametric Bayesian mixture models
[{"from": "human", "value": "I am writing a research paper on a idea Incremental learning of nonparametric Bayesian mixture models"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Clustering is a fundamental task in many vision applications. To date, most clustering algorithms work in a batch setting and training examples must be gathered in a large group before learning can begin. Here  explore incremental clustering, in which data can arrive continuously.  present a novel incremental model-based clustering algorithm based on nonparametric Bayesian methods, which  call memory bounded variational Dirichlet process (MB-VDP). The number of clusters are determined flexibly by the data and the approach can be used to automatically discover object categories. The computational requirements required to produce model updates are bounded and do not grow with the amount of data processed. The technique is ll suited to very large datasets, and  show that our approach outperforms existing online alternatives for learning nonparametric Bayesian mixture models."}]