SLEP: Sparse Learning with Efficient Projections

By Jun Liu, Shuiwang Ji, Jieping Ye. 2011

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Citations in the corpus

Spectral Regularization Algorithms for Learning Large Incomplete Matrices (2010, methodology): “Note that for all the examples considered in Table 3, the implementations of algorithms NESTEROV (Liu et al., 2009; Ji and Ye, 2009) and MMMF (Rennie and Srebro, 2005) are prohibitively expensive both in terms of computational time and memory requirements, and hence could not be run.”
Integrating low-rank and group-sparse structures for robust multi-task learning (2011, background): “APM has attracted extensive attentions in the machine learning and data mining communities [20, 19, 15, 8, 18, 21] due to its optimal convergence rate among all first-order techniques and its ability of dealing with large-scale non-smooth optimization problems.”
Cross-View Action Recognition via Transferable Dictionary Learning (2016, methodology): “We use the software SLEP in [27] for computing the sparse codes.”
Moreau-Yosida Regularization for Grouped Tree Structure Learning (2010, methodology): “The source codes, included in the SLEP package [12], are available online1.”
Deep ensemble learning of sparse regression models for brain disease diagnosis (2017, background): “htm,’ where the control parameter is required to be set between 0 and 1 because its value is internally rescaled (Liu et al., 2010).”
Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices (2015, methodology): “The solver is from the SLEP package (Liu et al., 2009c).”
Multi-Modal Multi-Task Learning for Joint Prediction of Multiple Regression and Classification Variables in Alzheimer’s Disease (2011, methodology): “At present, there are many algorithms developed to solve MTFS; in this paper we adopt the SLEP toolbox (Liu et al., 2009), which has been shown very effective on many datasets.”
Robust multi-task feature learning (2012, methodology): “Here we propose to employ the accelerated gradient descent [25, 26] to solve the optimization problem, which generates the solution at the k-th iteration (k ≥ 1) by computing the following proximal operator [20, 19, 21, 12, 37, 3]:”
Modeling disease progression via multi-task learning (2013, unknown): “We employ the algorithm in the SLEP package (Liu et al., 2009), which computes the proximal operator associated with the general ‘1/‘q-norm efficiently.”
On the Linear Convergence of a Proximal Gradient Method for a Class of Nonsmooth Convex Minimization Problems (2013, methodology): “We refer readers to [6, 7, 13, 16, 18, 20–22] for further studies on group Lasso type of statistical techniques.”
Learning View-Invariant Sparse Representations for Cross-View Action Recognition (2013, methodology): “Y V k ]−Dkαk|| 2 F + λ||αk||1 5: Initialize class-specific dictionary D k in D v by solving D k = argminDv k ,βv k ||Y v k −D v kβ v k || 2 F + λ||βk||1 6: end for 7: repeat 8: Compute sparse codes xvi , z v i of a set of correspondence videos y v i by solving the multi-task group LASSO problem in (”
Data X-Ray: A Diagnostic Tool for Data Errors (2015, methodology): “We use the SLEP package implementation for logistic regression [38] and WEKA [ ?”
Face Recognition Across Non-Uniform Motion Blur, Illumination, and Pose (2015, methodology): “nnLeastR function of the Lasso algorithm [31] which considers”
An efficient algorithm for a class of fused lasso problems (2010, methodology): “The Nesterov’s method has been applied to solve various sparse learning formulations [2, 13, 16, 17, 18, 22, 32].”
Distribution Calibration in Riemannian Symmetric Space (2011, background): “Cross-domain learning has widely been applied to machine learning [7], [18], pattern recognition [25], [31], [32], and image processing [8], [9], [17], [27], particularly for the case when it is relatively difficult or expensive to collect labeled training samples.”
Efficient multi-task feature learning with calibration (2014, methodology): “Thus, we can use the framework of FISTA [4, 24, 12, 2] to solve the dual problem in Eq.”
A Multi-Task Learning Formulation for Survival Analysis (2016, background): “(12) is a special case of the non-negative max-heap projection which can be eﬃciently solved [18, 17], and some details of the non-negative max-heap projection can be found in Appendix.”