Discovering communities is an essential step in the analysis of complex systems, and it has two purposes: to identify functional modules and to interpret semantics. However, most of the existing community detection methods only focused on identify comm…
Supervised discrete hashing (SDH) has widely attracted attention because it can directly obtain hash codes rather than relaxing the discrete constraint, which reduces information loss during the quantization process. However, SDH and its extensions ten…
The rapid progress of urbanization has expedited the process of urban planning, e.g., new residential, commercial areas, which in turn boosts the local travel demand. We propose a novel “off-deployment traffic estimation problem”, namely,…
With the rapid development of web technology, social media platforms have become a breeding ground for rumors. These rumors can threaten people’s health, endanger the economy, and affect the stability of a country. In recent years, to mitigate t…
Entity Set Expansion (ESE) is the problem that expands a small set of seed entities into a more complete set, entities of which have common traits. As a popular data mining task, ESE has been widely used in many applications, such as dictionary constru…
In this big data era, knowledge becomes increasingly linked, along with the rapid growth in data volume. Connected knowledge is naturally represented and stored as knowledge graphs, which are of more and more importance for many frontier research areas…
How to extract useful knowledge from a high-dimensional and sparse (HiDS) matrix efficiently is critical for many big data-related applications. A latent factor (LF) model has been widely adopted to address this problem. It commonly relies on an iterative learning algorithm like stochastic gradient descent. However, an algorithm of this kind commonly consumes many iterations to converge, resulting in considerable time cost on large-scale datasets. How to accelerate an LF model’s training process without accuracy loss becomes a vital issue. To address it, this study innovatively proposes a multilayered-and-randomized latent factor (MLF) model. Its main idea is two-fold: a) adopting randomized-learning to train LFs for implementing a ‘one-iteration’ training process for saving time; and 2) adopting the principle of a generally multilayered structure as in a deep forest or multilayered extreme learning machine to structure its LFs, thereby enhancing its representative learning ability. Empirical studies on six HiDS matrices from real applications demonstrate that compared with state-of-the-art LF models, an MLF model achieves significantly higher computational efficiency with satisfactory prediction accuracy. It has the potential to handle LF analysis on a large scale HiDS matrix with real-time requirements.
The ever-increasing popularity of web APIs allows app developers to leverage a set of existing web APIs to achieve their sophisticated objectives. The heavily fragmented distribution of web APIs makes it challenging for an app developer to find appropr…
Recently, many deep reinforcement learning (DRL)-based task scheduling algorithms have been widely used in edge computing (EC) to reduce energy consumption. Unlike the existing algorithms considering fixed and fewer edge nodes (servers) and tasks, in t…
The importance of wild video based image set recognition is monotonically increasing due to the large amount of video data being collected by various devices including surveillance cameras, drive recorders, smart phones, and internet. The content of th…