Submodular functions characterize the diminishing returns effect and are widely applied in many fields such as economics, combinatorial optimization, information theory, and machine learning. Hence, research on submodular functions attracts significant attention. In recent years, the scale of problems that need to be addressed has gradually increased with the advancement of computer science. Moreover, the functions that need to be optimized in real-world scenarios often exhibit properties similar to submodular functions but do not strictly conform to the definition of submodular functions. Therefore, designing fast algorithms for set functions that are close to submodular properties has become a research focus. For the problem of optimizing monotone non-submodular functions subject to matroid constraints subject to matroid constraints, with a Diminishing-Return (DR) ratio of γ , this paper presents the γ-MatroidContinuousGreedy Algorithm (γ-MCG Algorithm). This algorithm is a nearly-linear time approximation algorithm with an approximation ratio of (γ2(1 − 1/𝑒)2 − O(ϵ)). Notably, it is the first known nearly-linear time algorithm for graph matroid constraints and partition matroid constraints.
Mixup is a learning principle that trains a neural network on convex combinations of pairs of examples and their labels. Despite of its good performance, there is an inherent inconsistency between training and testing in mixup, which makes theoretical understanding difficult and hurts the performance in some cases. In this work, we propose λ-mixup to alleviate this inconsistency. Specifically, λ-mixup reformulates the model to take the interpolation coefficient (𝜆) as input as well, so that a class of models indexed by 𝜆 is learned and we can select one specific coefficient or multiple coefficients for ensembles depending on the testing distribution. We theoretically demonstrate that, with enough data and model capacity, λ-mixup can recover the original conditional distribution. Moreover, we conduct image classification tasks on multiple datasets, including CIFAR-10, CIFAR-100 and Tiny-Imagenet, showing that comparing with mixup, λ-mixup exhibits better generalization, calibration and robustness to adversarial attacks and out-of-distribution transformations.
Partial fingerprint identification technology which is mainly used in device with small sensor area like cellphone, U disk and computer, has taken more attention in recent years with its unique advantages. However, owing to the lack of sufficient minutiae points, the conventional method do not perform well in the above situation. We propose a new fingerprint matching technique which utilizes ridges as features to deal with partial fingerprint images and combines the modified generalized Hough transform and scoring strategy based on machine learning. The algorithm can effectively meet the real-time and space-saving requirements of the resource constrained devices. Experiments on in-house database indicate that the proposed algorithm have an excellent performance.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.