A novel adaptive subspace ensemble slow feature regression model was developed for soft sensing application.Compared to traditional single models and random subspace models,the proposed method is improved in three asp...A novel adaptive subspace ensemble slow feature regression model was developed for soft sensing application.Compared to traditional single models and random subspace models,the proposed method is improved in three aspects.Firstly,sub-datasets are constructed through slow feature directions and variables in each subdatasets are selected according to the output related importance index.Then,an adaptive slow feature regression is presented for sub-models.Finally,a Bayesian inference strategy based on a slow feature analysis process that monitors statistics is developed for probabilistic combination.Two industrial examples were used to evaluate the proposed method.展开更多
As a kind of statistical method, the technique of Hidden Markov Model （HMM） is widely used for speech recognition. In order to train the HMM to be more effective with much less amount of data, the Subspace Distribut...As a kind of statistical method, the technique of Hidden Markov Model （HMM） is widely used for speech recognition. In order to train the HMM to be more effective with much less amount of data, the Subspace Distribution Clustering Hidden Markov Model （SDCHMM）, derived from the Continuous Density Hidden Markov Model （CDHMM）, is introduced. With parameter tying, a new method to train SDCHMMs is described. Compared with the conventional training method, an SDCHMM recognizer trained by means of the new method achieves higher accuracy and speed. Experiment results show that the SDCHMM recognizer outperforms the CDHMM recognizer on speech recognition of Chinese digits.展开更多
The RGB2GRAY conversion model is the most popular and classical tool for image decolorization. A recent study showed that adapting the three weighting parameters in this first-order linear model with a discrete search...The RGB2GRAY conversion model is the most popular and classical tool for image decolorization. A recent study showed that adapting the three weighting parameters in this first-order linear model with a discrete searching solver has a great potential in its c6nversion ability. In this paper, we present a two-step strategy to efficiently extend the parameter searching solver to a two-order multivariance polynomial model, as a sum of three subspaces. We show that the first subspace in the two-order model is the most important and the second one can be seen as a refinement. In the first stage of our model, the gradient correlation similarity （Gcs） measure is used on the first subspace to obtain an immediate grayed image. Then, Gcs is applied again to select the optimal result from the immettiate grayed image plus the second subspace-induced candidate images. Experimental results show the advantages of the proposed approach in terms of quantitative evaluation, qualitative evaluation, and algorithm complexity.展开更多
基金the support from the National Natural Science Foundation of China(No.21676086).
文摘A novel adaptive subspace ensemble slow feature regression model was developed for soft sensing application.Compared to traditional single models and random subspace models,the proposed method is improved in three aspects.Firstly,sub-datasets are constructed through slow feature directions and variables in each subdatasets are selected according to the output related importance index.Then,an adaptive slow feature regression is presented for sub-models.Finally,a Bayesian inference strategy based on a slow feature analysis process that monitors statistics is developed for probabilistic combination.Two industrial examples were used to evaluate the proposed method.
基金Supported by the National Natural Science Foundation of China (No.60172048)
文摘As a kind of statistical method, the technique of Hidden Markov Model （HMM） is widely used for speech recognition. In order to train the HMM to be more effective with much less amount of data, the Subspace Distribution Clustering Hidden Markov Model （SDCHMM）, derived from the Continuous Density Hidden Markov Model （CDHMM）, is introduced. With parameter tying, a new method to train SDCHMMs is described. Compared with the conventional training method, an SDCHMM recognizer trained by means of the new method achieves higher accuracy and speed. Experiment results show that the SDCHMM recognizer outperforms the CDHMM recognizer on speech recognition of Chinese digits.
基金Project supported by the National- Basic Research Program （973） of China （No. 2013CB035600）, the National Natural Science Foundation of China （Nos. 61261010, 61362001, and 61503176）, Jiangxi Provincial Advanced Projects for Post-Doctoral Research Funds of China （No. 2014KY02）, the International Postdoctoral Exchange Fellowship Program, and the International Scientific and Technological Cooperation Projects of Jiangxi Province, China （No. 20141BDH80001）
文摘The RGB2GRAY conversion model is the most popular and classical tool for image decolorization. A recent study showed that adapting the three weighting parameters in this first-order linear model with a discrete searching solver has a great potential in its c6nversion ability. In this paper, we present a two-step strategy to efficiently extend the parameter searching solver to a two-order multivariance polynomial model, as a sum of three subspaces. We show that the first subspace in the two-order model is the most important and the second one can be seen as a refinement. In the first stage of our model, the gradient correlation similarity （Gcs） measure is used on the first subspace to obtain an immediate grayed image. Then, Gcs is applied again to select the optimal result from the immettiate grayed image plus the second subspace-induced candidate images. Experimental results show the advantages of the proposed approach in terms of quantitative evaluation, qualitative evaluation, and algorithm complexity.
