Efficient Similarity Search in Nonmetric Spaces with Local Constant Embedding

Similarity-based search has been a key factor for many applications such as multimedia retrieval, data mining, Web search and retrieval, and so on. There are two important issues related to the similarity search, namely, the design of a distance function to measure the similarity and improving the search efficiency. Many distance functions have been proposed, which attempt to closely mimic human recognition. Unfortunately, some of these well-designed distance functions do not follow the triangle inequality and are therefore nonmetric. As a consequence, efficient retrieval by using these nonmetric distance functions becomes more challenging, since most existing index structures assume that the indexed distance functions are metric. In this paper, we address this challenging problem by proposing an efficient method, that is, local constant embedding (LCE), which divides the data set into disjoint groups so that the triangle inequality holds within each group by constant shifting. Further...

• Type: Journal
• Year: 2008
• Where: TKDE
• Authors: Lei Chen 0002, Xiang Lian
• Keywords: Distance Functions, Nonmetric Distance Functions, Similarity Search, TKDE 2008