Adaptive Local Low-rank Matrix Approximation for Recommendation
Huafeng Liu, Liping Jing, Yuhua Qian, Jian Yu
Low-rank matrix approximation (LRMA) has attracted more and more attention in the community of recommendation. Even though LRMA-based recommendation methods (including Global LRMA and Local LRMA) obtain promising results, ...
ACM Transactions on Information Systems, 2019, 37(4), 45.
Intuitionistic fuzzy rough set-based granular structures and attribute subset selection
Anhui Tan, Weizhi Wu, Yuhua Qian, Jiye Liang, Jinkun Chen, Jinjin Li
Attribute subset selection is an important issue in dataminingandinformationprocessing.However,mostautomatic methodologies consider only the relevance factor between samples while ignoring the diversity factor. This may not allow the utilization value of hidden information to be exploited. For this reason, we propose a hybrid model named intuitionistic fuzzy (IF) rough set to overcome this limitation. The model combines the technical advantages of rough set and IF set and can effectively consider the above-mentioned statistical factors.
IEEE Transactions on Fuzzy Systems, 2019, 27(3), 527-539.
Clustering ensemble based on sample's stability
Feijiang Li, Yuhua Qian, Jieting Wang, Chuangyin Dang, Liping Jing
The objective of clustering ensemble is to fifind the underlying structure of data based on a set of clustering results. It has been observed that the samples can change between clusters in different clustering results. This change shows that samples may have different contributions to the detection of the underlying structure.
Artificial Intelligence, 2019, 273, 37-55.
Diversity-induced fuzzy clustering
Honghong Cheng, Yuhua Qian
Granular computing plays an important role in human reasoning and problem solving, a reasonable granulation method is important in practical tasks.
International Journal of Approximate Reasoning,2019, 106，89-106.
Environmental sound classification with dilated convolutions
In sound information retrieval (SIR) area, environmental sound classification (ESC) emerges as a new issue, which aims at classifying environments by analysing the complex features extracted from the various sound data.
Applied Acoustics,2019, 148, 123-132.