Outlier Path: A Homotopy Algorithm for Robust SVM
Authors: Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Ichiro Takeuchi
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compared the proposed outlier-path (OP) algorithm with the concave-convex procedure (CCCP) (Yuille & Rangarajan, 2002). In most of the existing RSVM studies, CCCP or a variant called difference of convex (DC) programming are used for optimizing RSVM (Shen et al., 2003; Krause & Singer, 2004; Liu et al., 2005; Liu & Shen, 2006; Collobert et al., 2006; Wu & Liu, 2007). We used the 10 benchmark data sets listed in Table 1. We randomly divided each data set into the training (40%), validation (30%), and test (30%) sets for training, model selection (including the selection of θ or s), and performance evaluation, respectively. |
| Researcher Affiliation | Academia | Shinya SUZUMURA suzumura.mllab.nit@gmail.com Nagoya Institute of Technology Gokiso-cho, Showa-ku, Nagoya, Aichi 466 8555 Japan Kohei OGAWA ogawa.mllab.nit@gmail.com Nagoya Institute of Technology Gokiso-cho, Showa-ku, Nagoya, Aichi 466 8555 Japan Masashi Sugiyama sugi@cs.titech.ac.jp Tokyo Institute of Technology O-okayama, Meguro-ku, Tokyo 152-8552, Japan Ichiro Takeuchi takeuchi.ichiro@nitech.ac.jp Nagoya Institute of Technology Gokiso-cho, Showa-ku, Nagoya, Aichi 466 8555 Japan |
| Pseudocode | Yes | Algorithm 1 Outlier Path Algorithm Algorithm 2 Continuous Step (C-step) Algorithm 3 Discontinuous Step (D-step) |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We used the 10 benchmark data sets listed in Table 1. ... Table 1. Benchmark data sets. n and d denote the number of instances and the input dimensionality, respectively. Data D1 Breast Cancer Diagnostic ... D2 Australian Credit Approval ... D5 Spambase ... D7 Gisette ... |
| Dataset Splits | Yes | We randomly divided each data set into the training (40%), validation (30%), and test (30%) sets for training, model selection (including the selection of θ or s), and performance evaluation, respectively. |
| Hardware Specification | No | The paper discusses computational time and presents results in Figure 5, but it does not specify any hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies, libraries, or solvers used in their implementation. |
| Experiment Setup | Yes | In all the setups, the regularization parameter C was chosen from {0.01, 0.1, 1, 10, 100}, while the candidates of the homotopy parameter θ or s were set as follows: In OP-θ, all the break-points θBP were considered as the candidates (note that the local solutions at each break-point have been already computed in the homotopy computation). In OP-s, all the break-points for s BP between sinit := mini Nn yi f(xi) and 0 are considered as the candidates. |