Jie Yang

TL;DR: A new supervised two-step learning framework, Posterior Distribution Learning (PDL), is proposed to build a robust supervised model in data space by first describing a new constrained graph method to estimate the posterior probability of the unlabeled samples in learning set and then extending a real function regression model to a vector-valued function model to fit a nonlinear function for the posterior probabilities distribution in the input data space.

TL;DR: An effective multi-class moving target recognition method that is based on Gaussian mixture part-based model, which accurately locates objects of interest and recognises their corresponding categories in video sequences is proposed.

TL;DR: NMF’s variants into the visual tracking framework in the view of data clustering for appearance modeling and an inverse NMF model is proposed in which each learned base vector is regarded as a clustering center in a low-dimensional subspace.

TL;DR: It can be seen that the proposed method was superior to the traditional spectra analysis and envelope demodulation methods in detecting the weak periodic signal and indicates a promising prospect for mechanical fault monitoring and diagnosis.

TL;DR: This work proposes a novel algorithm, MIL-SKDE (multiple-instance learning with supervised kernel density estimation), which addresses MIL problem through an extended framework of ''KDE (kernel density estimation)+mean shift''.