The discriminant ability of features plays a central role in classification tasks. Deep learning (DL) utilizes back-propagation to yield discriminant features with an end-to-end depth structure, which lacks interpretability. A new learning paradigm, Green Learning (GL), has been proposed to address the weaknesses of DL, including high carbon footprints, large model sizes, and mathematical opaqueness. Following this idea, a statistics-based feature generation (SFG) method is proposed to boost system performance further. SFG consists of two modules: 1) feature subset selection and 2) discriminant feature generation via linear combination. In the first module, SFG traces the splitting features along individual tree paths within auxiliary XGBoost classifiers, which form the desired feature subsets for each binary label subspace. In the second module, SFG determines the weights of features in each subspace by formulating a least-square normal equation supervised by its corresponding binary sub-labels. SFG enables the adaptive assessment of feature importance at global and various local scales. Besides, it reduces the computational requirement by working in low-dimensional feature sub-spaces. Extensive experiments are conducted on classical image classification tasks to demonstrate the effectiveness and efficiency of SFG.

Teams of embodied AI-enabled agents are critical for applications in extreme and highly dynamic environments. Developing robust controllers for such agents requires a deep understanding of the challenges encountered when attempting to coordinate and synchronize their individual perception-cognition-communication-action (PCCA) loops for team-wide mission objectives. We introduce a framework to explore the coordination of the PCCA loops across multiple agents in a new simulated physical environment designed to explore collaboration in each PCCA stage. This environment tasks teams of agents with the correct disposal of dangerous objects in an area and forces careful coordination of sensing, communication, movement, and manipulation actions by providing spatially-bounded communication, incorporating situations that require concerted effort by groups of agents, and introducing uncertainty into agents’ sensing capabilities. We provide a set of heuristic controllers, an offline oracle model, and an initial exploration of a Reward Machine-based controller that learns its policies from training. Together these approaches serve to provide insights into the complexity of the multi-agent PCCA loop coordination problem. The multiagent PCCA simulation environment, which supports AI and human-controlled agents, and the code for various agent controllers are available at https://github.com/nesl/AI-Collab.

AI algorithms at the edge demand smaller model sizes and lower computational complexity. To achieve these objectives, we adopt a green learning (GL) paradigm rather than the deep learning paradigm. GL has three modules: 1) unsupervised representation learning, 2) supervised feature learning, and 3) supervised decision learning. We focus on the second module in this work. In particular, we derive new discriminant features from proper linear combinations of input features, denoted by x, obtained in the first module. They are called complementary and raw features, respectively. Along this line, we present a novel supervised learning method to generate highly discriminant complementary features based on the least-squares normal transform (LNT). LNT consists of two steps. First, we convert a C-class classification problem to a binary classification problem. The two classes are assigned with 0 and 1, respectively. Next, we formulate a least-squares regression problem from the N-dimensional (N-D) feature space to the 1-D output space, and solve the least-squares normal equation to obtain one N-D normal vector, denoted by a1. Since one normal vector is yielded by one binary split, we can obtain M normal vectors with M splits. Then, Ax is called an LNT of x, where transform matrix A$\in R^{M\times N}$ by stacking $\mathrm{a}_{J}^{T}$, j=1,, M, and the LNT, Ax, can generate M new features. The newly generated complementary features are shown to be more discriminant than the raw features. Experiments show that the classification performance can be improved by these new features.

Inspired by the decision learning process of multilayer per-ceptron (MLP) and decision tree (DT), a new classification model, named the subspace learning machine (SLM), is proposed in this work. SLM first identifies a discriminant subspace, S0, by examining the discriminant power of each input feature. Then, it learns projections of features in S0to yield 1D subspaces and finds the optimal partition for each. A criterion is developed to choose the best q partitions that yield 2qpartitioned subspaces. The partitioning process is recursively applied at each child node to build an SLM tree. When the samples at a child node are sufficiently pure, the partitioning process stops, and each leaf node makes a prediction. The ensembles of SLM trees can yield a stronger predictor. Extensive experiments are conducted for performance benchmarking among SLM trees, ensembles and classical classifiers.

Subspace partitioning in a high-dimensional feature space plays a fundamental role in the design of effective classifiers. A novel subspace learning machine (SLM) that projects high-dimensional feature vectors into a 1D feature subspace and partitions it into two disjoint sets was recently proposed. As an extension, SLM with soft partitioning, denoted by SLM/SP, is proposed in this work. SLM/SP adopts the soft decision tree (SDT) data structure for decision learning. It starts by learning an adaptive tree structure by using local greedy subspace partitioning. Once the stopping criteria are met for all child nodes and the tree structure is determined, all projection vectors are updated globally. This methodology enables efficient training, high classification accuracy, and a small model size. It is shown by experimental results that an SLM/SP tree offers a lightweight and high performance classification method.