Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours

By Feiping Nie, Guohao Cai, Xuelong Li. AAAI Conference on Artificial Intelligence, 2017

521 citations recorded by the source corpus.

Due to the efficiency of learning relationships and complex structures hidden in data, graph-oriented methods have been widely investigated and achieve promising performance in multi-view learning. Generally, these learning algorithms construct informative graph for each view or fuse different views to one graph, on which the following procedure are based. However, in many real world dataset, original data always contain noise and outlying entries that result in unreliable and inaccurate graphs, which cannot be ameliorated in the previous methods. In this paper, we propose a novel multi-view learning model which performs clustering/semi-supervised classification and local structure learning simultaneously. The obtained optimal graph can be partitioned into specific clusters directly. Moreover, our model can allocate ideal weight for each view automatically without additional weight and penalty parameters. An efficient algorithm is proposed to optimize this model. Extensive experimental results on different real-world datasets show that the proposed model outperforms other state-of-the-art multi-view algorithms.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/62b5d514ec49173af59cab6f0dfdc7f280d53f36

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. Ling Shao
2. Li Liu
3. En Zhu
4. Hong Liu
5. Siwei Wang
6. Wenxuan Tu
7. Fumin Shen
8. Jie Wen

In Computer Vision and Pattern Recognition

1. Ling Shao
2. Li Liu
3. Fumin Shen
4. Heng Tao Shen
5. Hong Liu
6. Jiancheng Lv
7. Zheng Zhang
8. Dacheng Tao

Citations in the corpus

• Multi-view clustering: A survey (2018, unknown): “In order to address these challenges, Nie et al.[63] developed a parameter-free multiple graph framework to learn a set of weights automatically for all graphs.”
• Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (2020, background): “data points into their respective groups without any label information [13]–[16].”
• Scalable Multi-view Subspace Clustering with Unified Anchors (2021, background): “Multi-view clustering, which integrates the diverse and complementary information among views for clustering, is an important unsupervised learning method in the machine learning and multimedia data mining community [16, 19, 22, 24, 28, 31].”
• Dual Contrastive Prediction for Incomplete Multi-View Representation Learning (2022, background): “(6) MLAN (Multi-view Learning with Adaptive Neighbours) [75] per-”
• Fast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance (2021, methodology): “Some recent methods accelerate the time complexity into O(n(3)) per iteration [3], [15], [24]–[29].”
• Binary Multi-View Clustering (2019, methodology): “, k-means) to group data; the second category is the multi-view spectral clustering methods [11], [19], [20], [21], [22], which construct multiple graphs to characterize the geometrical structure and employ an existing clustering method for data partition; the third category is based on matrix facto”