Yi Yang
Beihang University
14 Papers
12 Citations
Yi Yang is an academic researcher from Beihang University. The author has contributed to research in topics: Cluster analysis & Computer science. The author has an hindex of 5, co-authored 7 publications. Previous affiliations of Yi Yang include University of British Columbia.
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Papers
Incomplete multi-view clustering via deep semantic mapping
TL;DR: A novel incompletemulti-view clustering method, which projects all incomplete multi-view data to a complete and unified representation in a common subspace, and a new objective function is developed and the optimization processes are presented.
107
Co-Learning Non-Negative Correlated and Uncorrelated Features for Multi-View Data
TL;DR: A non-negative correlated and uncorrelated feature co-learning (CoUFC) method that identifies view-specific features for each view when learning the common feature across views in the latent semantic subspace and outperforms the state-of-the-art multiview learning methods.
44
ICFS Clustering With Multiple Representatives for Large Data
TL;DR: This paper discusses two challenges, i.e., assignment of new arriving objects and dynamic adjustment of clusters, in incremental CFS (ICFS) clustering, and proposes two ICFS clustering algorithms, ICFS with multiple representatives (ICfsMR) and the enhanced ICFSMR (E_ICFSMR) to tackle the two challenges.
42
Fluctuation analysis of instantaneous availability under specific distribution
TL;DR: The problem on the early fluctuation of instantaneous availability (IA) is considered, where it is for a one-unit repairable system, and by transforming the renewal equation into differential equations, the instantaneous availability can be given under specific distribution.
9
Parameter-Free Incremental Co-Clustering for Multi-Modal Data in Cyber-Physical-Social Systems
Liang Zhao,Zhikui Chen,Yi Yang +2 more
TL;DR: The proposed parameter-free incremental co-clustering method outperforms the compared state-of-the-art methods in terms of effectiveness and efficiency, thus it is promising for clustering dynamic multi-modal data in cyber-physical-social systems.
9