9 citations · 38 across the 27 of their papers we have counts for
11 papers · 1 filter
Multi-View Multiple Clusterings using Deep Matrix Factorization
Shaowei Wei, Jun Wang, Guoxian Yu +2
Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results. Existing multi-view clustering solutions can on…
Active Multi-Label Crowd Consensus
Jinzheng Tu, Guoxian Yu, Carlotta Domeniconi +2
Crowdsourcing is an economic and efficient strategy aimed at collecting annotations of data through an online platform. Crowd workers with different expertise are paid for their se…
Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection
Hamed Sarvari, Carlotta Domeniconi, Bardh Prenkaj +1
Autoencoders, as a dimensionality reduction technique, have been recently applied to outlier detection. However, neural networks are known to be vulnerable to overfitting, and ther…
Estimator Vectors: OOV Word Embeddings based on Subword and Context Clue Estimates
Raj Patel, Carlotta Domeniconi
Semantic representations of words have been successfully extracted from unlabeled corpuses using neural network models like word2vec. These representations are generally high quali…
Cross-modal Zero-shot Hashing
Xuanwu Liu, Zhao Li, Jun Wang +3
Hashing has been widely studied for big data retrieval due to its low storage cost and fast query speed. Zero-shot hashing (ZSH) aims to learn a hashing model that is trained using…
Weakly-paired Cross-Modal Hashing
Xuanwu Liu, Jun Wang, Guoxian Yu +2
Hashing has been widely adopted for large-scale data retrieval in many domains, due to its low storage cost and high retrieval speed. Existing cross-modal hashing methods optimisti…
ActiveHNE: Active Heterogeneous Network Embedding
Xia Chen, Guoxian Yu, Jun Wang +3
Heterogeneous network embedding (HNE) is a challenging task due to the diverse node types and/or diverse relationships between nodes. Existing HNE methods are typically unsupervise…
Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization
Yuying Xing, Guoxian Yu, Carlotta Domeniconi +3
Multi-view Multi-instance Multi-label Learning(M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple…
Multi-View Multiple Clustering
Shixing Yao, Guoxian Yu, Jun Wang +2
Multiple clustering aims at exploring alternative clusterings to organize the data into meaningful groups from different perspectives. Existing multiple clustering algorithms are d…
Ranking-based Deep Cross-modal Hashing
Xuanwu Liu, Guoxian Yu, Carlotta Domeniconi +3
Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are…
Multiple Independent Subspace Clusterings
Xing Wang, Jun Wang, Carlotta Domeniconi +3
Multiple clustering aims at discovering diverse ways of organizing data into clusters. Despite the progress made, it's still a challenge for users to analyze and understand the dis…