Spectral Embedded Clustering: A Framework for In-Sample and Out-of-Sample Spectral Clustering

By Feiping Nie, Zinan Zeng, Ivor Wai-Hung Tsang, Dong Xu, Changshui Zhang. IEEE Transactions on Neural Networks, 2011

318 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/247c90aabda047a6545774a33e55199fd378d5b5

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Citations in the corpus

Improved Deep Embedded Clustering with Local Structure Preservation (2017, methodology): “SEC is a variant of spectral clustering with a linearity regularization explicitly added and outperforms traditional spectral clustering methods on a wide range of datasets according to [Nie et al., 2011].”
Learning to Locate Relative Outliers (2011, background): “Moreover , as shown in (Nie et al., 2011), learning the label of unlabeled data and the hyperplane classifier together can achieve better generalization performance.”
Unifying Virtual and Physical Worlds (2017, methodology): “…online and ofﬂine social afﬁnity graphs A v ∈ R N × N and A p ∈ R N × N by calculating their pairwise similarities with the Gaussian similarity function [Nie et al. 2011a, 2015, 2016], where the radius parameters θ v and θ p are simply set as the median of the Euclidean distances of all user…”
Parameter-Free Auto-Weighted Multiple Graph Learning: A Framework for Multiview Clustering and Semi-Supervised Classification (2016, background): “Some other works clustered data in high dimension [Nie et al., 2011] or construct the data similarity matrix by adaptively selecting more reliable neighbors [Nie et al., 2014].”
Clustering and projected clustering with adaptive neighbors (2014, background): “In the past decades, many clustering algorithms have been proposed, such as hierarchical clustering [10], K-means clustering [13], spectral clustering [15], spectral embedded clustering [19], support vector clustering [1], maximum margin clustering [22], initialization independent clustering [18], m”
Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization (2017, background): “Spectral clustering [Ng et al., 2001; Zelnik-Manor and Perona, 2004; Cai and Chen, 2015; Nie et al., 2011] aims at exploring the local nonlinear manifold (spectral graph)1 structure [Hou et al.”
Large-Scale Multi-View Spectral Clustering via Bipartite Graph (2015, methodology): “To address this problem, several methods have been proposed, e.g. (Passerini, Pontil, and Frasconi 2004; Fowlkes et al. 2004; Alzate and Suykens 2010; Bengio et al. 2004; Nie et al. 2011).”
Deep Clustering with Convolutional Autoencoders (2017, methodology): “For the sake of completeness, two traditional and classic clustering algorithms, k-means and Spectral Embedded Clustering (SEC) [11], are also included in comparison.”
Structured Graph Learning for Scalable Subspace Clustering: From Single View to Multiview (2021, unknown): “Another usually ignored aspect is the out-of-sample problem [20], that is, the existing framework does not generalize to unseen data points.”
Contrastive Clustering (2021, result): “Although some promising results have been achieved recently (Nie et al. 2011; Liu et al. 2016; Nie et al. 2016; Liu, Shen, and Tsang 2017; Wang et al. 2020), most of the algorithms would produce undesirable results due to the over-high complexity in real-world datasets.”
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization (2017, methodology): “For example, DEC , SEC , and LDMGI report their best results by tuning one hyper-parameter over nine different options, and JULE-SF and JULE-RC achieve their good performance by tweak-ing several hyper-parameters over various datasets.”
Unsupervised Deep Embedding for Clustering Analysis (2015, methodology): “L G ] 24 M a y 2016 2011), and REUTERS (Lewis et al., 2004), comparing it with standard and state-of-the-art clustering methods (Nie et al., 2011; Yang et al., 2010).”