Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction

By Feiping Nie, Dong Xu, Ivor Wai-Hung Tsang, Changshui Zhang. IEEE Transactions on Image Processing, 2010

495 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Inter-media hashing for large-scale retrieval from heterogeneous data sources (2013, background): “Previous research works have shown the benefit and success of exploiting the local structural information within a single data type (especially for high-dimensional multimedia data) in various applications, ranging from feature selection to annotation and retrieval [22, 25, 32, 35, 36, 34].”
Multiple feature hashing for real-time large scale near-duplicate video retrieval (2011, background): “As demonstrated in [12, 15, 26, 27, 34, 35], it is beneﬁcial to exploit the local structure of the training data to infer an accurate and compact representation, especially for multimedia data.”
Flexible Orthogonal Neighborhood Preserving Embedding (2017, methodology): “For FME, we set each parameter to {10−9, 10−6, 10−3, 100, 103, 106, 109}, and then we report the best accuracy as its final result.”
Unsupervised Feature Selection with Structured Graph Optimization (2016, methodology): “We adopt 2,1-norm regularization (Nie et al. 2010a) on W to make it row sparse.”
Unsupervised Large Graph Embedding (2017, methodology): “2007) and Flexible Manifold Embedding (FME) (Nie et al. 2010), respectively.”
Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection (2014, background): “They are often lack of using the learning mechanism as in feature learning, which is proved to be powerful and widely used in many areas [24] [25].”