As an important topic in computer vision and multimedia analysis, 3D shape recognition has attracted much research attention recent years. For point cloud data multiview data, various approaches have been proposed with remarkable performance. However, few works simultaneously employ the to represent shapes, which is complementary beneficial our consideration. Moreover, existing multimodal mainl...

A weakly-supervised learning framework named as complementary-label has been proposed recently, where each sample is equipped with a single complementary label that denotes one of the classes does not belong to. However, existing methods cannot learn from easily accessible unlabeled samples and multiple labels, which are more informative. In this paper, to remove these limitations, we propose n...

Cambridge undergraduates have regular active-learning opportunities in small-group tutorials, which they solve problems and discuss ideas based on course material. Would see any value performing similar tasks flipped-classroom settings, or would regard the introduction of a second modality as redundant? Following replacement traditional lectures with flipped teaching within three physiology cou...

Swarm Intelligence (SI) is an innovative artificial intelligence technique for solving complex optimization problems. Data clustering is the process of grouping data into a number of clusters. The goal of data clustering is to make the data in the same cluster share a high degree of similarity while being very dissimilar to data from other clusters. Clustering algorithms have been applied to a ...

This study explores the linguistic application of bipartite spectral graph partitioning, a graphtheoretic technique that simultaneously identifies clusters of similar localities as well as clusters of features characteristic of those localities. We compare the results using this approach to previously published results on the same dataset using cluster and principal component analysis (Shacklet...

Cluster, the noun, has several connotations. For one, a cluster is a collection of things of the same kind, originally of grapes, but also of fruits or flowers, growing closely together. The relevant connotation here is, however, "A number of persons, animals, or things gathered or situated close together; an assemblage, group, swarm, crowd" (Oxford English Dictionary on the web). Of course, Po...

Machine-learning solutions to classification, clustering and matching problems critically depend on the adopted metric, which in the past was selected heuristically. In the last decade, it has been demonstrated that an appropriate metric can be learnt from data, resulting in superior performance as compared with traditional metrics. This has recently stimulated a considerable interest in the to...

Identifying clusters is an important aspect of data analysis. This paper proposes a noveldata clustering algorithm to increase the clustering accuracy. A novel game theoretic self-organizingmap (NGTSOM ) and neural gas (NG) are used in combination with Competitive Hebbian Learning(CHL) to improve the quality of the map and provide a better vector quantization (VQ) for clusteringdata. Different ...

Sorting and clustering large numbers of documents can be an overwhelming task: manual solutions tend to be slow, while machine learning systems often present results that don‘t align well with users‘ intents. We created and evaluated a system for helping users sort large numbers of documents into clusters. iCluster has the capability to recommend new items for existing clusters and appropriate ...