2009 Ninth IEEE International Conference on Data Mining Non-negative Laplacian Embedding

Citations in the corpus

• Novel Class Discovery without Forgetting (2022, methodology): “Following the practice in Clustering and NCD approaches [19,16,35,49], we use clustering accuracy to measure the performance of class discovery on unlabeled data.”
• Multi-Label Learning with Global and Local Label Correlation (2017, background): “The manifold regularizer in (3) can be equivalently written as trðF0 L0F 0Þ [30], where L0 1⁄4 D0 S0 is the l l label Laplacian matrix of S0.”
• A Nonlinear Orthogonal Non-Negative Matrix Factorization Approach to Subspace Clustering (2017, methodology): “Furthermore, KNSC-Ncut has a soft clustering intepretation [1, 24, 23] where a data point could belong fractionally to more than one cluster.”