Clustering and projected clustering with adaptive neighbors

By Feiping Nie, Xiaoqian Wang, Heng Huang. Knowledge Discovery and Data Mining, 2014

1,005 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ed3a8f2a15fa1cf32976f78e9c506f08a950a932

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. Feiping Nie
2. Xuelong Li
3. Yi Yang
4. Jing Li
5. Deng Cai
6. Zhou Zhao
7. Hanqing Lu
8. Wei Zhu

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Yi Yang
3. Xuelong Li
4. Feiping Nie
5. Luc Van Gool
6. Ming-Hsuan Yang
7. Cordelia Schmid
8. Xiaogang Wang

In Statistical and Nonlinear Physics

1. Xuelong Li
2. Feiping Nie
3. Jing Li
4. Wei Zhu
5. Yi Yang
6. Guohao Cai
7. Heng Huang
8. Kun Zhan

Citations in the corpus

• The Constrained Laplacian Rank Algorithm for Graph-Based Clustering (2016, methodology): “Under this constraint, the learned S is block diagonal with proper permutation, and thus we can directly partition the data points into k clusters based on S (Nie, Wang, and Huang 2014).”
• Large-Scale Multi-View Spectral Clustering via Bipartite Graph (2015, background): “Recently, spectral clustering (SC) is drawing more and more attention because of its effectiveness (Shi and Malik 2000; Von Luxburg 2007; Ng et al. 2002; Zelnik-Manor and Perona 2004; Nie, Wang, and Huang 2014; Chang et al. 2015).”
• Partial Multi-Modal Sparse Coding via Adaptive Similarity Structure Regularization (2016, methodology): “That is, AdaPM(2)SC directly learns a similarity induced probability neighborhood [6], where the data similarity between different modalities is computed by joint probability strategy.”
• Multiview Consensus Graph Clustering (2019, methodology): “According to [9] and [11], the projection problem Eq.”
• Unsupervised Feature Selection with Structured Graph Optimization (2016, methodology): “Based on the KKT condition, the optimal solution of si is sij = (− dij 2αi + θ)+, (25) where θ = 1k + 1 2kαi ∑k j=1 dij (Nie, Wang, and Huang 2014).”
• Graph Learning for Multiview Clustering (2018, neutral): “In practice, we determine the two parameters in a heuristic way to accelerate the procedure [8].”
• Self-weighted Multiview Clustering with Multiple Graphs (2017, background): “Recently, [Nie et al., 2014a; Feng et al., 2014; Chen and Dy, 2016; Nie et al., 2016b] have leveraged this property in different ways.”
• Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours (2017, methodology): “In view of the above consideration, (Nie, Wang, and Huang 2014) added a rank constrain to the LS in problem 1 according to the Theorem 1: min si∈n×1 n∑ i,j ||xi − xj ||22sij + α||S||2F s.t. ∀i, sTi 1 = 1, 0 ≤ sij ≤ 1, rank(LS) = n− c (3) It assigns adaptive neighbours to each of samples, which…”
• Semi-Supervised Learning With Graph Learning-Convolutional Networks (2019, neutral): “(5)), as discussed in [17].”
• An Adaptive Semisupervised Feature Analysis for Video Semantic Recognition (2018, methodology): “the strategies used in [41], we formulate the Lagrangian function of objective function (8) as:”