Multiple Incomplete Views Clustering via Weighted Nonnegative Matrix Factorization with L2, 1 Regularization

By Weixiang Shao, Lifang He, Philip S. Yu. ECML/PKDD, 2015

311 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/f5cb8683245eba3e874fe53e1cca17e66d06423d

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. Lei Wang
2. Xinwang Liu
3. Hong Liu
4. Huaimin Wang
5. Xi Peng
6. Jie Wen
7. Dinggang Shen
8. Chang-Fu Tang

In Computer Vision and Pattern Recognition

1. Hong Liu
2. Lei Wang
3. Xinwang Liu
4. Huaimin Wang
5. Xi Peng
6. Jie Wen
7. Dinggang Shen
8. Chang-Fu Tang

In Media Technology

1. Hong Liu
2. Xi Peng
3. Jie Wen
4. Dinggang Shen
5. Chang-Fu Tang
6. Boyun Li
7. Yuanbiao Gou
8. Jiancheng Lv

Citations in the corpus

• Late Fusion Incomplete Multi-view Clustering (2018, methodology): “TABLE 1 Aggregated ACC, NMI and Purity Comparison (mean std) of Different Clustering Algorithms on Flower17 and Flower102 Datasets MKKM+ZF MKKM+MF MKKM+KNN MKKM+AF MKKM-IK MIC MVEC LF-IMVC [25] [30] [27] [14] Proposed ACC Flower17 36:90 0:77 36:75 0:57 37:78 0:61 40:48 0:73 44:63 0:57 34:33 2:19 22:”
• A Survey on Multi-View Clustering (2017, unknown): “Up to now, there have already been several multi-view works [23], [35], [36], [43], [74], [83], [85], [103] that attempted to solve the incomplete view problem.”
• COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction (2021, methodology): “We compare COMPLETER with 10 multi-view clustering approaches including Deep Canonically Correlated Analysis (DCCA) [1], Deep Canonically Correlated Au-toencoders (DCCAE) [42], Binary Multi-view Clustering (BMVC) [52], Autoencoder in Autoencoder Networks (AE 2 -Nets) [51], Partial Multi-View Cluster”
• Dual Contrastive Prediction for Incomplete Multi-View Representation Learning (2022, unknown): “, matrix factorization based methods [25], [26], [27], [28], kernel learning basedmethods [23], [29], spectral clusterinliuxinwangg based methods [22], and deep learning based methods [14], [30].”
• Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (2020, methodology): “technique to jointly learn the latent representations of different views and the consensus representation for all views [25].”