Learning with Augmented Features for Heterogeneous Domain Adaptation

We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. Using two different projection matrices, we first transform the data from two domains into a common subspace in order to measure the similarity between the data from two domains. We then propose two new feature mapping functions to augment the transformed data with their original features and zeros. The existing learning methods (e.g., SVM and SVR) can be readily incorporated with our newly proposed augmented feature representations to effectively utilize the data from both domains for HDA. Using the hinge loss function in SVM as an example, we introduce the detailed objective function in our method called Heterogeneous Feature Augmentation (HFA) for a linear case and also describe its kernelization in order to efficiently cope with the data with very high dimensions. Moreover, we also develop an alternating optimization algorithm to effectively solve the nontrivial optimization problem in our HFA method. Comprehensive experiments on two benchmark datasets clearly demonstrate that HFA outperforms the existing HDA methods.Comment: ICML201

Reflection methods for user-friendly submodular optimization

Recently, it has become evident that submodularity naturally captures widely occurring concepts in machine learning, signal processing and computer vision. Consequently, there is need for efficient optimization procedures for submodular functions, especially for minimization problems. While general submodular minimization is challenging, we propose a new method that exploits existing decomposability of submodular functions. In contrast to previous approaches, our method is neither approximate, nor impractical, nor does it need any cumbersome parameter tuning. Moreover, it is easy to implement and parallelize. A key component of our method is a formulation of the discrete submodular minimization problem as a continuous best approximation problem that is solved through a sequence of reflections, and its solution can be easily thresholded to obtain an optimal discrete solution. This method solves both the continuous and discrete formulations of the problem, and therefore has applications in learning, inference, and reconstruction. In our experiments, we illustrate the benefits of our method on two image segmentation tasks.Comment: Neural Information Processing Systems (NIPS), \'Etats-Unis (2013

Programmable Insight: A Computational Methodology to Explore Online News Use of Frames

abstract: The Internet is a major source of online news content. Online news is a form of large-scale narrative text with rich, complex contents that embed deep meanings (facts, strategic communication frames, and biases) for shaping and transitioning standards, values, attitudes, and beliefs of the masses. Currently, this body of narrative text remains untapped due—in large part—to human limitations. The human ability to comprehend rich text and extract hidden meanings is far superior to known computational algorithms but remains unscalable. In this research, computational treatment is given to online news framing for exposing a deeper level of expressivity coined “double subjectivity” as characterized by its cumulative amplification effects. A visual language is offered for extracting spatial and temporal dynamics of double subjectivity that may give insight into social influence about critical issues, such as environmental, economic, or political discourse. This research offers benefits of 1) scalability for processing hidden meanings in big data and 2) visibility of the entire network dynamics over time and space to give users insight into the current status and future trends of mass communication.Dissertation/ThesisDoctoral Dissertation Computer Science 201