A Constrained Feature Selection Approach Based on Feature Clustering and Hypothesis Margin Maximization

In this paper, we propose a semisupervised feature selection approach that is based on feature clustering and hypothesis margin maximization. The aim is to improve the classification accuracy by choosing the right feature subset and to allow building more interpretable models. Our approach handles the two core aspects of feature selection, i.e., relevance and redundancy, and is divided into three steps. First, the similarity weights between features are represented by a sparse graph where each feature can be reconstructed from the sparse linear combination of the others. Second, features are then hierarchically clustered identifying groups of the most similar ones. Finally, a semisupervised margin-based objective function is optimized to select the most data discriminative feature from within each cluster, hence maximizing relevance while minimizing redundancy among features. Eventually, we empirically validate our proposed approach on multiple well-known UCI benchmark datasets in terms of classification accuracy and representation entropy, where it proved to outperform four other semisupervised and unsupervised methods and competed with two widely used supervised ones.

Locomotion approach of bipedal robot utilizing passive wheel without swing leg based on stability margin maximization and fall prevention functions

This paper proposes a locomotion approach of leg-wheel robot utilizing passive wheel attached to the foot of bipedal robot. The key feature of this approach is bipedal mobility without swing leg. This mobility contributes the stability based on expansion of support polygon during locomotion, the robustness for obstacles and stopping to prevent fall, and the adaptability by prevention of body swing sideways. To achieve these, we propose the stability margin maximization to optimize center of gravity projection for support polygon and the fall prevention functions for real environment that is a difficult situation to prevent unexpected fall by the only planning. Finally, we apply the proposed methods to leg-wheel phases through locomotion and verify the contribution by experiments using real bipedal robot.

Locomotion approach of bipedal robot utilizing passive wheel without swing leg based on stability margin maximization and fall prevention functions

This paper proposes a locomotion approach of leg-wheel robot utilizing passive wheel attached to the foot of bipedal robot. The key feature of this approach is bipedal mobility without swing leg. This mobility contributes the stability based on expansion of support polygon during locomotion, the robustness for obstacles and stopping to prevent fall, and the adaptability by prevention of body swing sideways. To achieve these, we propose the stability margin maximization to optimize center of gravity projection for support polygon and the fall prevention functions for real environment that is a difficult situation to prevent unexpected fall by the only planning. Finally, we apply the proposed methods to leg-wheel phases through locomotion and verify the contribution by experiments using real bipedal robot.

Maximum Margin Multi-Dimensional Classification

Multi-dimensional classification (MDC) assumes heterogenous class spaces for each example, where class variables from different class spaces characterize semantics of the example along different dimensions. Due to the heterogeneity of class spaces, the major difficulty in designing margin-based MDC techniques lies in that the modeling outputs from different class spaces are not comparable to each other. In this paper, a first attempt towards maximum margin multi-dimensional classification is investigated. Following the one-vs-one decomposition within each class space, the resulting models are optimized by leveraging classification margin maximization on individual class variable and model relationship regularization across class variables. We derive convex formulation for the maximum margin MDC problem, which can be tackled with alternating optimization admitting QP or closed-form solution in either alternating step. Experimental studies over real-world MDC data sets clearly validate effectiveness of the proposed maximum margin MDC techniques.

IMMIGRATE: A Margin-Based Feature Selection Method with Interaction Terms

Traditional hypothesis-margin researches focus on obtaining large margins and feature selection. In this work, we show that the robustness of margins is also critical and can be measured using entropy. In addition, our approach provides clear mathematical formulations and explanations to uncover feature interactions, which is often lack in large hypothesis-margin based approaches. We design an algorithm, termed IMMIGRATE (Iterative max-min entropy margin-maximization with interaction terms), for training the weights associated with the interaction terms. IMMIGRATE simultaneously utilizes both local and global information and can be used as a base learner in Boosting. We evaluate IMMIGRATE in a wide range of tasks, in which it demonstrates exceptional robustness and achieves the state-of-the-art results with high interpretability.