Noisy-label data inevitably gives rise to confusion in various perception applications. In this work, we revisit the theory of support vector machines (SVM) which mines support vectors to build the maximum-margin hyperplane for robust classification, and propose a robust-to-noise deep learning framework, SV-Learner, including the Support Vector Contrastive Learning (SVCL) and Support Vector-based Noise Screening (SVNS). The SV-Learner mines support vectors to solve the learning problem with noisy labels (LNL) reliably. Support Vector Contrastive Learning (SVCL) adopts support vectors as positive and negative samples, driving robust contrastive learning to enlarge the feature distribution margin for learning convergent feature distributions. Support Vector-based Noise Screening (SVNS) uses support vectors with valid labels to assist in screening noisy ones from confusable samples for reliable clean-noisy sample screening. Finally, Semi-Supervised classification is performed to realize the recognition of noisy samples. Extensive experiments are evaluated on CIFAR-10, CIFAR-100, Clothing1M, and Webvision datasets, and results demonstrate the effectiveness of our proposed approach. The source code is availablehttps://github.com/yanliji/SV-Learner.

Sparse optimization is a central problem in machine learning and computer vision. However, this problem is inherently NP-hard and thus difficult to solve in general. Combinatorial search methods find the global optimal solution but are confined to small-sized problems, while coordinate descent methods are efficient but often suffer from poor local minima. This paper considers a new block decomposition algorithm that combines the effectiveness of combinatorial search methods and the efficiency of coordinate descent methods. Specifically, we consider a random strategy or/and a greedy strategy to select a subset of coordinates as the working set, and then perform a global combinatorial search over the working set based on the original objective function. We show that our method finds stronger stationary points than Amir Beck et al.'s coordinate-wise optimization method. In addition, we establish the convergence rate of our algorithm. Our experiments on solving sparse regularized and sparsity constrained least squares optimization problems demonstrate that our method achieves state-of-the-art performance in terms of accuracy. For example, our method generally outperforms the well-known greedy pursuit method.

In this paper, we propose a novel data stream clustering algorithm, termed SVStream, which is based on support vector domain description and support vector clustering. In the proposed algorithm, the data elements of a stream are mapped into a kernel space, and the support vectors are used as the summary information of the historical elements to construct cluster boundaries of arbitrary shape. To adapt to both dramatic and gradual changes, multiple spheres are dynamically maintained, each describing the corresponding data domain presented in the data stream. By allowing for bounded support vectors (BSVs), the proposed SVStream algorithm is capable of identifying overlapping clusters. A BSV decaying mechanism is designed to automatically detect and remove outliers (noise). We perform experiments over synthetic and real data streams, with the overlapping, evolving, and noise situations taken into consideration. Comparison results with state-of-the-art data stream clustering methods demonstrate the effectiveness and efficiency of the proposed method.