An incremental multilinear system for human face learning and recognition

This dissertation establishes a novel system for human face learning and recognition based on incremental multilinear Principal Component Analysis (PCA). Most of the existing face recognition systems need training data during the learning process. The system as proposed in this dissertation utilizes an unsupervised or weakly supervised learning approach, in which the learning phase requires a minimal amount of training data. It also overcomes the inability of traditional systems to adapt to the testing phase as the decision process for the newly acquired images continues to rely on that same old training data set. Consequently when a new training set is to be used, the traditional approach will require that the entire eigensystem will have to be generated again. However, as a means to speed up this computational process, the proposed method uses the eigensystem generated from the old training set together with the new images to generate more effectively the new eigensystem in a so-called incremental learning process. In the empirical evaluation phase, there are two key factors that are essential in evaluating the performance of the proposed method: (1) recognition accuracy and (2) computational complexity. In order to establish the most suitable algorithm for this research, a comparative analysis of the best performing methods has been carried out first. The results of the comparative analysis advocated for the initial utilization of the multilinear PCA in our research. As for the consideration of the issue of computational complexity for the subspace update procedure, a novel incremental algorithm, which combines the traditional sequential Karhunen-Loeve (SKL) algorithm with the newly developed incremental modified fast PCA algorithm, was established. In order to utilize the multilinear PCA in the incremental process, a new unfolding method was developed to affix the newly added data at the end of the previous data. The results of the incremental process based on these two methods were obtained to bear out these new theoretical improvements. Some object tracking results using video images are also provided as another challenging task to prove the soundness of this incremental multilinear learning method.

The impact of institutional complexity and top management characteristics on executive compensation and firm performance

While most studies take a dyadic view when examining the environmental difference between the home country of a multinational enterprise (MNE) and a particular foreign country, they ignore that an MNE is managing a network of subsidiaries embedded in diverse environments. Additionally, neither the impacts of global environments on top executives nor the effects of top executives’ capabilities to handle institutional complexity are fully explored. Thus, using a three-essay format, this dissertation tried to fill these gaps by addressing the effects of institutional complexity and top management characteristics on top executive compensation and firm performance. ^ Essay 1 investigated the impact of an MNE’s institutional complexity, or the diversity of national institutions facing an MNE’s network of subsidiaries, on the top management team (TMT) compensation. This essay proposed that greater political and cultural complexity leads to not only greater TMT total compensation but also to a greater portion of TMT compensation linked with long-term performance. The arguments are supported in this essay by using an unbalanced panel dataset including 296 U.S. firms with 1,340 observations. ^ Essay 2 explored TMT social capital and its moderating role on value creation and appropriation by the chief executive officer (CEO). Using a sample with 548 U.S. firms and 2,010 observations, it found that greater TMT social capital does facilitate the effects of CEO intellectual capital and social capital on firm growth. Finally, essay 3 examined the performance implications for the fit between managerial information-processing capabilities and institutional complexity. It proposed that institutional complexity is associated with the needs of information-processing. On the other hand, smaller TMT turnover and larger TMT size reflect larger managerial information-processing capabilities. Consequently, superior performance is achieved by the match among institutional complexity, TMT turnover, and TMT size. All hypotheses in essay 3 are supported in a sample of 301 U.S. firms and 1,404 observations. ^ To conclude, this dissertation advances and extends our knowledge on the roles of institutional environments and top executives on firm performance and top executive compensation.^