Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: How can  train a statistical mixture model on a massive data set? ,  show how to construct coresets for mixtures of Gaussians and natural generalizations. A coreset is a ighted subset of the data, which guarantees that models fitting the coreset will also provide a good fit for the original data set.  show that, perhaps surprisingly, Gaussian mixtures admit coresets of size independent of the size of the data set. More precisely,  prove that a ighted set of O(dk3/e2) data points suffices for computing a (1 + e)-approximation for the optimal model on the original n data points. Moreover, such coresets can be efficiently constructed in a map-reduce style computation, as ll as in a streaming setting. Our results rely on a novel reduction of statistical estimation to problems in computational geometry, as ll as new complexity results about mixtures of Gaussians.  empirically evaluate our algorithms on several real data sets, including a density estimation problem in the context of earthquake detection using accelerometers in mobile phones.
['Dan Feldman', 'Matthew Faulkner', 'Andreas Krause']
I am writing a research paper on a idea Scalable Training of Mixture Models via Coresets
[{"from": "human", "value": "I am writing a research paper on a idea Scalable Training of Mixture Models via Coresets"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: How can  train a statistical mixture model on a massive data set? ,  show how to construct coresets for mixtures of Gaussians and natural generalizations. A coreset is a ighted subset of the data, which guarantees that models fitting the coreset will also provide a good fit for the original data set.  show that, perhaps surprisingly, Gaussian mixtures admit coresets of size independent of the size of the data set. More precisely,  prove that a ighted set of O(dk3/e2) data points suffices for computing a (1 + e)-approximation for the optimal model on the original n data points. Moreover, such coresets can be efficiently constructed in a map-reduce style computation, as ll as in a streaming setting. Our results rely on a novel reduction of statistical estimation to problems in computational geometry, as ll as new complexity results about mixtures of Gaussians.  empirically evaluate our algorithms on several real data sets, including a density estimation problem in the context of earthquake detection using accelerometers in mobile phones."}]