Shi Yan's research while affiliated with Technical University of Denmark and other places
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Publications (4)
In this paper, we propose a new method to solve the minimization problem in a simultaneous reconstruction and segmentation (SRS) model for X-ray computed tomography (CT). The SRS model uses Bayes' rule and the maximum a posteriori (MAP) estimate on the hidden Markov measure field model (HMMFM). The original method [Romanov M, Dahl AB, Dong Y, Hanse...
In this paper, we propose a new simultaneous reconstruction and segmentation (SRS) model in X-ray computed tomography (CT). The new SRS model is based on the Gaussian mixture model (GMM). In order to transform non-separable log-sum term in GMM into a form that can be easy solved, we introduce an auxiliary variable, which in fact plays a segmentatio...
In this paper, we propose a fast method for simultaneous reconstruction and segmentation (SRS) in X-ray computed tomography (CT). Our work is based on the SRS model where Bayes' rule and the maximum a posteriori (MAP) are used on hidden Markov measure field model (HMMFM). The original method leads to a logarithmic-summation (log-sum) term, which is...
In this paper, we consider an unconstrained ℓ2,q minimization for group sparse signal recovery. For this nonconvex and non-Lipschitz problem, we mainly focus on its local minimizers. Firstly, a uniform lower bound for nonzero groups of the local minimizers is presented. Secondly, under group restricted isometry property (GRIP) assumptions, we provi...
Citations
... In detail, the outlined identification strategy takes advantages of the flexibility and efficient modeling capabilities characterizing the Gaussian Mixture Models (GMMs). These statistical tools, indeed, allow to approximate any given probability density with high accuracy [11]; and, for this reason, they are exploited in a large variety of applications ranging from path planning [12] to object tracking [13], from image modeling and segmentation [14] to speech understanding [15]. Coping with the SM identification problem, a GMM is adopted for describing the VSN collected data, thus leading to the design of a learning based strategy, which involves the presence of an auto-encoder (AE) to deal with the emergence of possible dimensionality issues. ...
... Data quality has a significant impact on predictive accuracy [47]. Most credit risk assessment studies have used a feature selection step as a preprocessing step to clean their data from any noise that may interfere with the training process [48][49][50][51][52][53][54][55][56][57][58][59]. Some scholars have also designed models based on highly noisy data. ...
