Kernel collaborative representation based dictionary learning and discriminative projection
نویسندگان
چکیده
منابع مشابه
KCRC-LCD: Discriminative kernel collaborative representation with locality constrained dictionary for visual categorization
We consider the image classification problem via kernel collaborative representation classification with locality constrained dictionary (KCRC-LCD). Specifically, we propose a kernel collaborative representation classification (KCRC) approach in which kernel method is used to improve the discrimination ability of collaborative representation classification (CRC). We then measure the similaritie...
متن کاملOnline Semi-Supervised Discriminative Dictionary Learning for Sparse Representation
We present an online semi-supervised dictionary learning algorithm for classification tasks. Specifically, we integrate the reconstruction error of labeled and unlabeled data, the discriminative sparse-code error, and the classification error into an objective function for online dictionary learning, which enhances the dictionary’s representative and discriminative power. In addition, we propos...
متن کاملDiscriminative Collaborative Representation for Classification
The recently proposed l2-norm based collaborative representation for classification (CRC) model has shown inspiring performance on face recognition after the success of its predecessor — the l1-norm based sparse representation for classification (SRC) model. Though CRC is much faster than SRC as it has a closed-form solution, it may have the same weakness as SRC, i.e., relying on a “good” (prop...
متن کاملDecentralized Dynamic Discriminative Dictionary Learning
We consider discriminative dictionary learning in a distributed online setting, where a network of agents aims to learn a common set of dictionary elements of a feature space and model parameters while sequentially receiving observations. We formulate this problem as a distributed stochastic program with a non-convex objective and present a block variant of the Arrow-Hurwicz saddle point algori...
متن کاملLow-rank representation based discriminative projection for robust feature extraction
The low-rank representation (LRR) was presented recently and showed effective and robust for subspace segmentation. This paper presents a LRR-based discriminative projection method (LRR-DP) for robust feature extraction, by virtue of the underlying low-rank structure of data represesntation revealed by LRR. LRR-DP seeks a linear transformation such that in the transformed space, the betweenlarg...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neurocomputing
سال: 2016
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2016.04.044