I'll put my code, presentation slides, useful links, and various other useful resources here.
My journal club/discussion class presentations
- "Stochastic Subgradient Descent for Nuclear Norm Regularization" [slides]
- Non-negative Matrix Factorization(NMF) and "A-Optimal Non-Negative Projection for Image Representation" [slides]
- Differential privacy tutorial [slides]
- Deep Learning and "Sparse modeling of human actions from motion imagery" [slides]
- Subspace Clustering with missing data: "High rank matrix completion" [slides]
- Discussion of Wiberg L1 (CVPR10 Best Paper) [slides]
Cool blog from the CMU statistician on statistics and machine learning topics.
A renowned machine learning theory blog. A few good/interesting posts per month. To have a flavor, check out the article: Adversarial Academia.
A comprehensive site that keeps updating the state-of-the-art algorithms, theory and evaluations in MF related fields, including: Matrix Completion, Matrix Recovery(RPCA), Compressive Sensing, Dictionary learning, Non-negative Matrix Factorization and etc.
The maintainer of Matrix Factorization Jungle (Igor Carron), articles are faster than updates on the summary site.
An almost thorough list of compressive sensing papers, reviews and tutorials.
A list of papers on nuclear norm based convex methods for low-rank matrix. Useful code samples of Augmented Lagrange Multiplier methods for RPCA.