Gradient Descent with Proximal Average for Nonconvex and Composite Regularization
Authors: Wenliang Zhong, James Kwok
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on a number of synthetic and real-world data sets demonstrate the effectiveness and efficiency of the proposed optimization algorithm, and also the improved classification performance resulting from the nonconvex regularizers. |
| Researcher Affiliation | Academia | Leon Wenliang Zhong James T. Kwok Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong {wzhong, jamesk}@cse.ust.hk |
| Pseudocode | No | The paper describes the proposed algorithms using mathematical equations and textual descriptions, but does not include a formal pseudocode block or an explicitly labeled 'Algorithm' section. |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | Experiments are performed on the 20newsgroup data set2, which contains 16,242 samples with 100 binary features (words). [...] 2http://www.cs.nyu.edu/ roweis/data.html |
| Dataset Splits | Yes | We use 1% of the data for training, 80% for testing, and the rest for validation. 40% of the data are randomly chosen for training, another 20% for validation, and the rest for testing. |
| Hardware Specification | Yes | Experiments are performed on a PC with Intel i7-2600K CPU and 32GB memory. |
| Software Dependencies | No | The paper states that 'All the algorithms are implemented in MATLAB, except for the proximal step in SCP which is based on the C++ code in the SLEP package (Liu, Ji, and Ye 2009)', but it does not specify version numbers for MATLAB, C++, or the SLEP package used in their implementation. |
| Experiment Setup | Yes | For (22), we vary (K, n) in {(5, 500), (10, 1000), (20, 2000), (30, 3000)}, and set λ = K/10, θ = 0.1. For (23), we set K = 10, n = 1000, and vary (λ, θ) in {(0.1, 0.1), (1, 10), (10, 10), (100, 100)}. The stepsize η is set to 1 2Lℓ, where Lℓis the largest eigenvalue of 1 n ST S. We set ηmax = 100 Lℓand ηmin = 0.01 Lℓ. As discussed before, we check condition (15) with f (rather than ˆf) and L = 10 5. |