Supervised Dictionary Learning
It is now well established that sparse signal models are well suited to restoration tasks and can effectively be learned from audio, image, and video data. Recent research has been aimed at learning discriminative sparse models instead of purely reconstructive ones. This paper proposes a new step in that direction with a novel sparse representation for signals belonging to different classes in terms of a shared dictionary and multiple decision functions. It is shown that the linear variant of the model admits a simple probabilistic interpretation, and that its most general variant also admits a simple interpretation in terms of kernels. An optimization framework for learning all the components of the proposed model is presented, along with experiments on standard handwritten digit and texture classification tasks.
Code (0)
등록된 구현이 없습니다.
Tasks
Dictionary LearningGeneral ClassificationTexture ClassificationSimilar Papers 제목 키워드 기반
Kernelized Supervised Dictionary Learning
In this paper, we propose supervised dictionary learning (SDL) by incorporating information on class labels into the learning of the dictionary. To this end, we propose to learn the dictionary in a space where the depend…
Dictionary LearningOn the Limitations of Unsupervised Bilingual Dictionary Induction
Unsupervised machine translation---i.e., not assuming any cross-lingual supervision signal, whether a dictionary, translations, or comparable corpora---seems impossible, but nevertheless, Lample et al. (2018) recently pr…
Graph SimilarityMachine TranslationTranslationUnsupervised Machine TranslationBilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences
In this paper, we propose a new task of machine translation (MT), which is based on no parallel sentences but can refer to a ground-truth bilingual dictionary. Motivated by the ability of a monolingual speaker learning t…
Machine TranslationTranslationWord TranslationAdaptively Unified Semi-Supervised Dictionary Learning With Active Points
Semi-supervised dictionary learning aims to construct a dictionary by utilizing both labeled and unlabeled data. To enhance the discriminative capability of the learned dictionary, numerous discriminative terms have been…
Dictionary LearningDLDL: Dynamic Label Dictionary Learning via Hypergraph Regularization
For classification tasks, dictionary learning based methods have attracted lots of attention in recent years. One popular way to achieve this purpose is to introduce label information to generate a discriminative diction…
Dictionary Learning