A Local Learning Approach for Clustering

By Mingrui Wu, Bernhard Schölkopf. Neural Information Processing Systems, 2006

301 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/adc52cc20595dc3fb5c9f672c169e1f837579e4f

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. Yi Yang
2. Dong Xu
3. Feiping Nie
4. Ivor Wai-Hung Tsang
5. Qiang Yang
6. Shiming Xiang
7. Yangqiu Song
8. Shuicheng Yan

In Computer Vision and Pattern Recognition

1. Yi Yang
2. Dacheng Tao
3. Luc Van Gool
4. Shuicheng Yan
5. Trevor Darrell
6. Feiping Nie
7. Xiaogang Wang
8. Ming-Hsuan Yang

Citations in the corpus

• Local and Global Regressive Mapping for Manifold Learning with Out-of-Sample Extrapolation (2010, methodology): “Note that our objective is intrinsically different from (Wu & Schölkopf 2006) which only utilizes a local learning model to predict the cluster identification of a single datum in Ni.”
• Image Clustering Using Local Discriminant Models and Global Integration (2010, methodology): “Similarly to [26], [27], and [33], for each data point, we consider a local clique.”
• Parallel Spectral Clustering in Distributed Systems (2011, unknown): “Following [47], this measure to evaluate the cluster quality is defined as Accuracy 1⁄4 Pn i1⁄41 ðyi;mapðciÞÞ n ; ð26Þ”
• Transferred Dimensionality Reduction (2008, methodology): “) 0.75 0.8 0.85 0.9 0.95 1 0.75 0.8 0.85 0.9 0.95 1 ACC of TDA ACC of DisKmeans AT&T 0.5 0.6 0.7 0.8 0.9 1 0.5 0.6 0.7 0.8 0.9 1 NMI of TDA NMI of DisKmeans AT&T 0.7 0.8 0.9 1 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 ACC of TDA ACC of DisKmeans Yale 0.5 0.6 0.7 0.8 0.5 0.55 0.6 0.65 0.7 0.75 0.8 NMI of TDA”
• Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms (2013, methodology): “Moreover, besides classification and regression problems, the local learning methods are also applied to some other learning problems, e.g., clustering [18] and dimensionality reduction [19].”
• Spectral Embedded Clustering: A Framework for In-Sample and Out-of-Sample Spectral Clustering (2011, methodology): “We compare the proposed SEC method with K -means (KM) clustering, DisKmeans (DKM) clustering [22], SC [10], local learning for (LL) clustering [45], and CLGR [37].”
• A review of unsupervised feature selection methods (2019, methodology): “The authors propose to use the Local Learning-Based Clustering (LLC) framework (Wu and Schölkopf 2007) to formulate the final ridge regression model.”
• An incremental learning vector quantization algorithm for pattern classification (2012, unknown): “tral prototypes in classification tasks [29].”