The backbone of the semantic web is ontology, dealing with the context of details associated
with a specific domain. Domain ontology (DO) is an important source of information for …

Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …

Text clustering is a task for grouping extracted information of the text in different clusters,
which has many applications in recommender systems, sentiment analysis, and more. Deep …

With the popularity of social platforms such as Sina Weibo, Tweet, etc., a large number of
public events spread rapidly on social networks and huge amount of textual data are …

Graph regularized nonnegative matrix factorization (GNMF) has been widely used in data
representation due to its excellent dimensionality reduction. When it comes to clustering …

Non-negative matrix factorization (NMF) efficiently reduces high dimensionality for many-
objective ranking problems. In multi-objective optimization, as long as only three or four …

Document representation is the basis of language modeling. Its goal is to turn natural
language text that flows into a structured form that can be stored and processed by a …

Labeling expensive and graph structure fuzziness are recognized as indispensable
prerequisites for solving practical problems in semi-supervised graph learning. This paper …

In general, fully exploiting the local structure of the original data space can effectively
improve the clustering performance of nonnegative matrix factorization (NMF). Therefore …

Several real-world phenomena, including social, communication, transportation, and
biological networks, can be efficiently expressed as graphs. This enables the deployment of …