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Relaxed Majorization-Minimization for Non-smooth and Non-convex Optimization
Chen Xu, Zhouchen Lin, Zhenyu Zhao +1
We propose a new majorization-minimization (MM) method for non-smooth and non-convex programs, which is general enough to include the existing MM methods. Besides the local majoriz…
Fast Proximal Linearized Alternating Direction Method of Multiplier with Parallel Splitting
Canyi Lu, Huan Li, Zhouchen Lin +1
The Augmented Lagragian Method (ALM) and Alternating Direction Method of Multiplier (ADMM) have been powerful optimization methods for general convex programming subject to linear…
Convex Sparse Spectral Clustering: Single-view to Multi-view
Canyi Lu, Shuicheng Yan, Zhouchen Lin
Spectral Clustering (SC) is one of the most widely used methods for data clustering. It first finds a low-dimensonal embedding of data by computing the eigenvectors of the norm…
Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm
Canyi Lu, Jinhui Tang, Shuicheng Yan +1
The nuclear norm is widely used as a convex surrogate of the rank function in compressive sensing for low rank matrix recovery with its applications in image recovery and signal pr…
Optimized Projections for Compressed Sensing via Direct Mutual Coherence Minimization
Canyi Lu, Huan Li, Zhouchen Lin
Compressed Sensing (CS) is a novel technique for simultaneous signal sampling and compression based on the existence of a sparse representation of signal and a projected dictionary…
Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis
Hongyang Zhang, Zhouchen Lin, Chao Zhang
Subspace recovery from corrupted and missing data is crucial for various applications in signal processing and information theory. To complete missing values and detect column corr…