In many high-dimensional big data clustering methods, subspace learning is a commonly used technique. Traditional subspace-based methods project high-dimensional data into low-dimensional space to perform dimensionality reduction and clustering. Dimensionality reduction can reduce computational complexity, but it also leads to the loss of some key features. To deal with this issue, we propose a novel clustering algorithm based on a hyperdisk representation that provides a tighter approximation of sample regions. Specifically, the hyperdisk is defined as the intersection between the affine packet and a hypersphere, forming a disk-like region that offers a more compact representation of the class boundaries. This model achieves a balance between the loose approximation of the affine packet and the strict constraints of the convex hull, thereby enhancing the stability and reliability of the algorithm in low-sample high-dimensional classification scenarios compared to traditional hyperellipse models. For optimization, a standard quadratic programming algorithm is utilized to solve the proposed formulation. The performance of the algorithm is comprehensively evaluated from multiple perspectives, and its effectiveness is demonstrated through extensive experimental results.

Efficient resource scheduling in Computing Power Networks (CPN) is very important for improving system performance and reducing energy consumption, which are two key factors for the development of low-consumption computing technologies. To address the challenges posed by sparse rewards in the CPN, we propose CPN Resource Scheduling with Reward Shaping (C2RS), which is a novel scheduler developed using Deep Reinforcement Learning (DRL). By incorporating a customized reward shaping mechanism, C2RS infers a dense reward function that is closely consistent with the original sparse reward signal. This approach is able to learn optimal scheduling policies faster and significantly improves the efficiency of baseline methods. Our comprehensive evaluation in the simulated CPN environment shows that C2RS outperforms existing DRL-based schedulers on multiple performance metrics. Importantly, C2RS not only improves learning efficiency, but also leads to the development of more effective scheduling policies through its innovative reward shaping, thereby contributing to the advancement of energy-efficient computing solutions for big data analytics.