On Spectral Clustering: Analysis and an algorithm

By Andrew Y. Ng, Michael I. Jordan, Yair Weiss. 2001

10,805 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

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Citations in the corpus

Inductive Representation Learning on Large Graphs (2017, neutral): “The majority of these approaches directly optimize the embeddings for each node using matrix-factorization-based objectives, and do not naturally generalize to unseen data, since they make predictions on nodes in a single, fixed graph [5, 11, 23, 28, 35, 36, 37, 39].”
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs (2016, background): “n the past decade. Modelling data as low-dimensional manifolds is the core of manifold learning techniques such as Laplacian eigenmaps [3] for non-linear dimensionality reduction, spectral clustering [34] or spectral hashing [57]. Signal processing on graphs. More recent works tried to generalize si”
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks (2015, methodology): “, 1985), to circle, spiral and ring datasets that helped motivate some of the most well-known clustering and semi-supervised learning algorithms ( Ng et al., 2002; Zhu et al. , 2003), Mackey Glass equations for time series ( Müller et al., 1997), and so on – in fact some of the well known UCI datase”
A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics (2012, methodology): “Normalized cut (NC) (Ng et al., 2001; Shi and Malik, 2000): For text clustering, the normalized cut model is usually formulated as computing the eigenvectors of”
Convergent Learning: Do different neural networks learn the same representations? (2015, background): “Does Relaxing the One-to-One Constraint to Find Many-to-Many Groupings Reveal More Similarities Between what Different Networks Learn? Since the preceding sections have shown that neurons may not necessarily correspond via a globally consistent one-to-one matching between networks, we now seek to fi”
Compositional Fairness Constraints for Graph Embeddings (2019, methodology): “Traditional approaches to this problem include Laplacian eigenmaps (Belkin & Niyogi, 2002) and matrix factorization techniques (Ng et al., 2001), with recent years witnessing a surge in methods that rely on random-walk based objectives (Grover & Leskovec, 2016; Perozzi et al., 2014), deep…”
Unmasking Clever Hans predictors and assessing what machines really learn (2019, methodology): “The SC [53,147,148] used in step (3) is a clustering algorithm with interesting analytic properties, which constitutes the foundation of the analysis pipeline.”
Joint Unsupervised Learning of Deep Representations and Image Clusters (2016, methodology): “We compare our approach with 12 clustering algorithms, including K-means [36], NJW spectral clustering (SCNJW) [40], self-tuning spectral clustering (SC-ST)[61], large-scale spectral clustering (SC-LS) [3], agglomerative clustering with average linkage (AC-Link)[24], Zeta function based agglomerativ”
Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice (2014, neutral): “Fusing multiple local features has turned out to be an effective method to boost the performance of recognition system in computer vision community [11,44, 41,47,6].”
A Comprehensive Survey on Transfer Learning (2019, methodology): “3) SFA: In this step, a spectral clustering algorithm is adapted and performed to align domain-specific features [108], [109].”
Community detection in graphs (2009, methodology): “In the algorithm by Ng et al. (Ng et al., 2001) one adopts the normalized Laplacian Lsym (Section A.2).”
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition (2016, background): “For example, people could adopt one of the cutting-edge unsupervised/semi-supervised clustering algorithms [21] [22] [23] [24] on our training dataset, and/or develop new algorithms which can accurately locate and remove outliers in a large, real dataset.”
Learning Discrete Representations via Information Maximizing Self-Augmented Training (2017, methodology): “…methods for clustering and hash learning can be broadly divided into two categories according to the decision boundaries they can model: linear (He et al., 2013; Gong et al., 2013; Norouzi & Blei, 2011) and non-linear (Xu et al., 2004; Kulis & Darrell, 2009; Ng et al., 2001; Weiss et al., 2009).”
Diverse Few-Shot Text Classification with Multiple Metrics (2018, methodology): “The matrix S is then cleaned and completed, giving a symmetry task similarity matrix Y for spectral clustering (Ng et al., 2002).”
SurfNet: Generating 3D Shape Surfaces Using Deep Residual Networks (2017, methodology): “Next, we perform spectral clustering [20] on the shape similarity matrix with K = 3 .”
Lazy Random Walks for Superpixel Segmentation (2014, positive): “According to the spectral graph theory [1], [4], the LRW algorithm converges to a unique stationary distribution π , which satisfies the following balance equation (see Lemma 2 in Appendix A):”