Robust regression methods for computer vision: a review
International Journal of Computer Vision
Convex Optimization
On Model Selection Consistency of Lasso
The Journal of Machine Learning Research
A fast and flexible method for the segmentation of aCGH data
Bioinformatics
Bioinformatics
Smoothing waves in array CGH tumor profiles
Bioinformatics
Bioinformatics
An efficient algorithm for a class of fused lasso problems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Spectral Regularization Algorithms for Learning Large Incomplete Matrices
The Journal of Machine Learning Research
Bioinformatics
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Foundations and Trends® in Machine Learning
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DNA copy number variation (CNV) accounts for a large proportion of genetic variation. One commonly used approach to detecting CNVs is array-based comparative genomic hybridization (aCGH). Although many methods have been proposed to analyze aCGH data, it is not clear how to combine information from multiple samples to improve CNV detection. In this paper, we propose to use a matrix to approximate the multisample aCGH data and minimize the total variation of each sample as well as the nuclear norm of the whole matrix. In this way, we can make use of the smoothness property of each sample and the correlation among multiple samples simultaneously in a convex optimization framework. We also developed an efficient and scalable algorithm to handle large-scale data. Experiments demonstrate that the proposed method outperforms the state-of-the-art techniques under a wide range of scenarios and it is capable of processing large data sets with millions of probes.