TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: Metric learning has been shown to be highly effective to improve the performance of nearest neighbor classification. In this paper, we address the problem of metric learning for symmetric positive definite (SPD) matrices such as covariance matrices, which arise in many real-world applications. Naively using standard Mahalanobis metric learning methods under the Euclidean geometry for SPD matrices is not appropriate, because the difference of SPD matrices can be a non-SPD matrix and thus the obtained solution can be uninterpretable. To cope with this problem, we propose to use a properly parameterized LogEuclidean distance and optimize the metric with respect to kernel-target alignment, which is a supervised criterion for kernel learning. Then the resulting non-trivial optimization problem is solved by utilizing the Riemannian geometry. Finally, we experimentally demonstrate the usefulness of our LogEuclidean metric learning algorithm on real-world classification tasks for EEG signals and texture patches.

SOLUTION: Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices

PROBLEM: Our hypothesis is that by equipping certain agents in a multi-agent system controlling an intelligent building with automated decision support, two important factors will be in creased. The first is energy saving in the building. The second is customer value-how the people in the building experience the ef fects of the actions of the agents. We give evi dence for the truth of this hypothesis through experimental findings related to tools for arti ficial decision making. A number of assump tions related to agent control, through moni toring and delegation of tasks to other kinds of agents, of rooms at a test site are relaxed. Each assumption controls at least one un certainty that complicates considerably the procedures for selecting actions part of each such agent. We show that in realistic deci sion situations, room-controlling agents can make bounded rational decisions even under dynamic real-time constraints. This result can be, and has been, generalized to other domains with even harsher time constraints.

SOLUTION: Artificial Decision Making Under Uncertainty in Intelligent Buildings

PROBLEM: Domain adaptation is important in sentiment analysis as sentiment-indicating words vary between domains. Recently, multi-domain adaptation has become more pervasive, but existing approaches train on all available source domains including dissimilar ones. However, the selection of appropriate training data is as important as the choice of algorithm. We undertake to our knowledge for the first time an extensive study of domain similarity metrics in the context of sentiment analysis and propose novel representations, metrics, and a new scope for data selection. We evaluate the proposed methods on two largescale multi-domain adaptation settings on tweets and reviews and demonstrate that they consistently outperform strong random and balanced baselines, while our proposed selection strategy outperforms instance-level selection and yields the best score on a large reviews corpus. All experiments are available at url_redacted1

SOLUTION:
Data Selection Strategies for Multi-Domain Sentiment Analysis