This paper presents a novel, simple yet robust self-representation method, ie, Double Low-
Rank Representation with Projection Distance penalty (DLRRPD) for clustering. With the …

Existing low-rank representation-based methods adopt a two-step framework, which must
employ an extra clustering method to gain labels after representation learning. In this paper …

Zero-Shot Learning (ZSL) has received extensive attention and successes in recent years
especially in areas of fine-grained object recognition, retrieval, and image captioning. Key to …

Cross-modal retrieval has attracted intensive attention in recent years, where a substantial
yet challenging problem is how to measure the similarity between heterogeneous data …

While large enterprises are benefiting from their global supply chains in these years, it is not
easy for Small and Medium-sized Enterprises (SMEs) to find supply chain partners. Treating …

Low-rank representation (LRR) is an effective method to learn the subspace structure
embedded in the data. However, most LRR methods make use of different features equally …

Prototype selection aims to remove redundancy and irrelevance from large-scale data by
selecting an informative subset, which makes it possible to see all data from a few …

In this paper, we give a new definition for sample complexity, and further develop a
theoretical analysis to bridge the gap between sample complexity and model capacity. In …

Prototype learning aims to eliminate redundancy of large-scale data by selecting an
informative subset. It is at the center of visual data analysis and processing. However, due to …

随着信息技术在社会各领域的深入渗透, 人类社会所拥有的数据总量达到了一个前所未有的高度
. 一方面, 海量数据为基于数据驱动的机器学习方法获取有价值的信息提供了充分的空间; …