# Generalized Adaptive Dictionary Learning via Domain Shift Minimization Source: [Generalized Adaptive Dictionary Learning via Domain Shift Minimization]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:48:56.275Z Authors: Varun Panaganti Published: 2014-10-31T20:42:36.000Z ## Abstract Visual data driven dictionaries have been successfully employed for various object recognition and classification tasks. However, the task becomes more challenging if the training and test data are from contrasting domains. In this paper, we propose a novel and generalized approach towards learning an adaptive and common dictionary for multiple domains. Precisely, we project the data from different domains onto a low dimensional space while preserving the intrinsic structure of data from each domain. We also minimize the domain-shift among the data from each pair of domains. Simultaneously, we learn a common adaptive dictionary. Our algorithm can also be modified to learn class-specific dictionaries which can be used for classification. We additionally propose a discriminative manifold regularization which imposes the intrinsic structure of class specific features onto the sparse coefficients. Experiments on image classification show that our approach fares better compared to the existing state-of-the-art methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()