The chemical sciences are producing an unprecedented amount of large, high-dimensional
data sets containing chemical structures and associated properties. However, there are …

Data similarity (or distance) computation is a fundamental research topic which underpins
many high-level applications based on similarity measures in machine learning and data …

Motivation Although modern high-throughput biomolecular technologies produce various
types of data, biosequence data remain at the core of bioinformatic analyses. However …

Networks are ubiquitous in the real world. Link prediction, as one of the key problems for
network-structured data, aims to predict whether there exists a link between two nodes. The …

Benefiting from the massive available data provided by Internet of multimedia things (IoMT),
enormous intelligent services requiring information of various types to make decisions are …

Abstract In recent years, Geo-Indistinguishability (GeoI) has been increasingly explored for
protecting location privacy in location-based services (LBSs). GeoI is considered a …

We introduce a new mathematical basis for similarity space. For the first time, we describe
the relationship between distance and similarity from set theory. Then, we derive generally …

In many search applications related to passage retrieval, text entailment, and subgraph
search, the query and each'document'is a set of elements, with a document being relevant if …

We present a new approach for independently computing compact sketches that can be
used to approximate the inner product between pairs of high-dimensional vectors. Based on …

The feature hashing algorithm introduced by Weinberger et al. is a popular dimensionality
reduction algorithm that compresses high dimensional data points into low dimensional data …