Semi-supervised structured output prediction by local linear regression and sub-gradient descent

Abstract

We propose a novel semi-supervised structured output prediction method based on local linear regression in this paper. The existing semi-supervise structured output prediction methods learn a global predictor for all the data points in a data set, which ignores the differences of local distributions of the data set, and the effects to the structured output prediction. To solve this problem, we propose to learn the missing structured outputs and local predictors for neighborhoods of different data points jointly. Using the local linear regression strategy, in the neighborhood of each data point, we propose to learn a local linear predictor by minimizing both the complexity of the predictor and the upper bound of the structured prediction loss. The minimization problem is solved by sub-gradient descent algorithms. We conduct experiments over two benchmark data sets, and the results show the advantages of the proposed method.

A Novel Transfer Learning Method Based on Common Space Mapping and Weighted Domain Matching

“…Experiments show its advantages over some other methods. In the future, we will extend the proposed algorithm to various applications, such as computational mechanic [8], [9], [10], [11], [12], [13], multimedia [14], [15], [16], [17], [18], [19], [20], [21], [22], medical imaging [23], [24], [25], [26], [27], [28], [29], [30], [31], bioinformatics [32], [33], [34], [35], material science [36], [37], [38], high-performance computing [39], [40], [41], [42], [43], malicious websites detection [44], [45], [46], [47], biometrics [48], [49], [50], [51], etc. We will also consider using some other models to represent and construction the classifier, such as Bayesian network [52], [53],…”