Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Human action recognition is a significant task in automatic understanding systems for video surveillance. Probabilistic Latent Semantic Analysis (PLSA) model has been used to learn and recognize human actions in videos. Specifically, PLSA employs the expectation maximization (EM) algorithm for parameter estimation during the training. The EM algorithm is an iterative estimation scheme that is guaranteed to find a local maximum of the likelihood function. Hover its convergence usually takes a large number of iterations. For action recognition with large amount of training data, this would result in long training time.  presents an incremental version of EM to speed up the training of PLSA without sacrificing performance accuracy. The proposed algorithm is tested on two challenging human action datasets. Experimental results demonstrate that the proposed algorithm converges with fer number of full passes compared with the batch EM algorithm. And the trained PLSA models achieve comparable or better recognition accuracies than those using batch EM training.
['Jie Xu', 'Getian Ye', 'Yang Wang', 'Gunawan Herman', 'Bang Zhang', 'Jun Yang']
I am writing a research paper on a idea Incremental EM for Probabilistic Latent Semantic Analysis on Human Action Recognition
[{"from": "human", "value": "I am writing a research paper on a idea Incremental EM for Probabilistic Latent Semantic Analysis on Human Action Recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Human action recognition is a significant task in automatic understanding systems for video surveillance. Probabilistic Latent Semantic Analysis (PLSA) model has been used to learn and recognize human actions in videos. Specifically, PLSA employs the expectation maximization (EM) algorithm for parameter estimation during the training. The EM algorithm is an iterative estimation scheme that is guaranteed to find a local maximum of the likelihood function. Hover its convergence usually takes a large number of iterations. For action recognition with large amount of training data, this would result in long training time.  presents an incremental version of EM to speed up the training of PLSA without sacrificing performance accuracy. The proposed algorithm is tested on two challenging human action datasets. Experimental results demonstrate that the proposed algorithm converges with fer number of full passes compared with the batch EM algorithm. And the trained PLSA models achieve comparable or better recognition accuracies than those using batch EM training."}]