Speedup Matrix Completion with Side Information: Application to Multi-Label Learning

By Miao Xu, Rong Jin, Zhi-Hua Zhou. 2013

305 citations recorded by the source corpus.

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Citations in the corpus

Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks (2017, methodology): “Classical Matrix Completion (MC) (Candes & Recht, 2012), Inductive Matrix Completion (IMC) (Jain & Dhillon, 2013; Xu et al., 2013), Geometric Matrix Completion (GMC) (Kalofolias et al., 2014), and Graph Reg-ularized Alternating Least Squares (GRALS) (Rao et al., 2015) were used as baseline methods.”
Learning and Refining of Privileged Information-Based RNNs for Action Recognition from Depth Sequences (2017, background): “This knowledge, also referred to as PI [41], hidden information [50] or side information [54], which can help to provide more explanations in training but will not be available at testing.”
The Emerging Trends of Multi-Label Learning (2020, background): “the missing entries of a matrix in matrix completion tasks [66].”
Multi-label Learning with Missing Labels Using Mixed Dependency Graphs (2018, background): “Second, filling in missing labels is treated as a matrix completion (MC) problem, including (Goldberg et al. 2010; Cabral et al. 2011; Xu et al. 2013).”
UvA-DARE ( Digital Academic Repository ) Graph Convolutional Matrix Completion (2017, unknown): “Inductive matrix completion (IMC) by Jain & Dhillon, 2013 and Xu et al., 2013 incorporates content information of users and items in feature vectors and approximates the observed elements of the rating matrix asMij = xTi UV T yj , with xi and yj representing the feature vector of user i and item j…”
Multi-Label Learning with Global and Local Label Correlation (2017, methodology): “Hence, we first use MAXIDE [21], a matrix completion algorithm for transductive multi-label learning, to fill in the missing labels before they can be applied.”
Dual-Primal Graph Convolutional Networks (2018, unknown): “MovieLens Flixster Douban Yahoo IMC [22, 45] 1.653 – – – GMC [24] 0.996 – – – MC [9] 0.973 – – – GRALS [39] 0”
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction (2016, methodology): “Furthermore, when features are known for the time series, we can make use of interaction models such as those in [6, 24, 25].”
Collaborative Filtering with Graph Information: Consistency and Scalable Methods (2015, methodology): “6 Experiments on Real Datasets Comparison to Related Formulations: We compare GRALS to other methods that incorporate side information for matrix completion: the ADMM method of [12] that regularizes the entire target matrix; using known features (IMC) [10, 24]; and standard matrix completion (MC).”