t-stochastic neighbor embedding (t-SNE) is a nonparametric data visualization method in classical machine learning. It maps the from high-dimensional space into low-dimensional space, especially two-dimensional plane, while maintaining relationship or similarities between surrounding points. In t-SNE, initial position of randomly determined, and achieved by moving to minimize cost function. Its...

Abstract The database object that describes tens of attributes is referred as high dimensional data space. In high dimensional data, the common distance measures can be influenced by noise. Existing clustering algorithms are implemented based on partitioning, hierarchical, density based and grid based. These methods assume some kind of cluster relationship among the clustered objects. Similarit...

Most of all index structures based on the R-tree have failed to support ecient indexing mechanisms for similarity search in high-dimensional data spaces. This is due to the fact that most of the index structures commonly use balanced split strategy in order to guarantee storage utilization and the shape of queries for similarity search is a hypersphere in high-dimensional spaces. In this paper...

Explicit semantic analysis (ESA) utilizes an immense Wikipedia index matrix in its interpreter part. This part of the analysis multiplies a large matrix by a term vector to produce a high-dimensional concept vector. A similarity measurement between two texts is performed between two concept vectors with numerous dimensions. The cost is expensive in both interpretation and similarity measurement...