Latent feature learning in social media network

By Zhaoquan Yuan, Jitao Sang, Yan Liu, Changsheng Xu. 2013

33 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Charu C. Aggarwal
2. Jiliang Tang
3. Dejing Dou
4. Nhathai Phan
5. Xintao Wu
6. Liang Wang
7. Wei Han
8. Guo-Jun Qi

In Statistical and Nonlinear Physics

1. Jiliang Tang
2. Charu C. Aggarwal
3. Nhathai Phan
4. Liang Wang
5. Wei Han
6. Dejing Dou
7. Guo-Jun Qi
8. Shiyu Chang

Citations in the corpus

• Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction (2016, background): “With significant success in recent years, the applications of deep learning are being expanded into other fields such as social media (Yuan et al. 2013), social network analysis (Perozzi, Al-Rfou, and Skiena 2014), bioinformatics (Chicco, Sadowski, and Baldi 2014), medicine and healthcare (Song et…”
• Heterogeneous Network Embedding via Deep Architectures (2015, methodology): “The most similar work to ours is that of [46].”
• Feature learning based on SAE-PCA network for human gesture recognition in RGBD images (2015, background): “Yuan et al. [30] proposed a generative framework to learn feature interaction to obtain the collective effect from multiple sources, so that tackle the analysis and applications in social media network.”
• A Comprehensive Survey on Cross-modal Retrieval (2016, methodology): “To predict the links between social media, Yuan et al. [33] design a Relational Generative Deep Belief Nets (RGDBN) model to learn latent features for social media, which utilizes the relationships between social media in the network.”
• Comparative Deep Learning of Hybrid Representations for Image Recommendations (2016, methodology): “One feasible solution that has been studied is to utilize the users’ information as constraints to refine the image representations, making them consistent with both semantic labels and user provided hints [19, 23, 33].”