{"slug": "sparse-and-robust-geometric-twin-support-vector-machine-via-asymmetric-roboss", "title": "Sparse and robust geometric twin support vector machine via asymmetric RoBoSS loss function", "summary": "Researchers proposed a novel asymmetric, robust, bounded, sparse and smooth (aR) loss function for an l1-norm penalized geometric twin support vector machine (aRSGTSVM) to handle classification and regression tasks, addressing redundant features, label noise, and feature noise. The method, detailed in arXiv:2608.11567v1, uses an l1-norm penalty for feature selection and a proximal gradient descent algorithm for optimization. Experiments on synthetic and UCI datasets showed superiority over state-of-the-art methods, and applications to index tracking in the China stock market achieved satisfactory performance.", "body_md": "arXiv:2608.11567v1 Announce Type: new\nAbstract: In real-world scenarios, the training data usually contains redundant features, label noise and feature noise, which provide severe challenges for the efficiency of machine learning methods. Since standard support vector machine (SVM) adopts $l_2$-norm penalty and hinge loss function, it lacks the ability of selecting significant features and is sensitive to noise. To address these issues, this paper proposes a novel asymmetric, robust, bounded, sparse and smooth (aR) loss function for $l_1$-norm penalized geometric twin SVM (aRSGTSVM) to handle classification and regression tasks. The $l_1$-norm penalty can achieve the feature selection. The proposed aR loss function can not only effectively mitigate the impact of label noise, but also significantly enhance the stability to resampling noise, i.e., the zero-mean feature noise around the boundary hyperplanes. Furthermore, a statistical analysis of the robustness of aRSGTSVM was also conducted using the influence function. Since aRSGTSVM involves nonconvex and nonsmooth optimization, we develop a fast and stable proximal gradient descent based solving algorithm. Compared with related state-of-the-art methods, experimental results demonstrate the superiority of the proposed aRSGTSVM on both synthetic and UCI datasets. Furthermore, we apply aRSGTSVM to index tracking tasks, where results for tracking the different indices in the China stock market show that it can achieve satisfactory performance.", "url": "https://wpnews.pro/news/sparse-and-robust-geometric-twin-support-vector-machine-via-asymmetric-roboss", "canonical_source": "https://www.machinebrief.com/news/sparse-and-robust-geometric-twin-support-vector-machine-via-qkwg", "published_at": "2026-08-13 04:00:00+00:00", "updated_at": "2026-08-13 04:41:30.310844+00:00", "lang": "en", "topics": ["machine-learning"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/sparse-and-robust-geometric-twin-support-vector-machine-via-asymmetric-roboss", "markdown": "https://wpnews.pro/news/sparse-and-robust-geometric-twin-support-vector-machine-via-asymmetric-roboss.md", "text": "https://wpnews.pro/news/sparse-and-robust-geometric-twin-support-vector-machine-via-asymmetric-roboss.txt", "jsonld": "https://wpnews.pro/news/sparse-and-robust-geometric-twin-support-vector-machine-via-asymmetric-roboss.jsonld"}}