Given their pervasive use, social media, such as Twitter, have become a leading source of breaking news. A key task in the automated identification of such news is the detection of novel documents from a voluminous stream of text documents in a scalable manner. Motivated by this challenge, we introduce the problem of online `1-dictionary learning where unlike traditional dictionary learning, wh...

Learning a dictionary of basis elements with the objective of building compact data representations is a problem of fundamental importance in statistics, machine learning and signal processing. In many settings, data points appear as a stream of high dimensional feature vectors. Streaming datasets present new twists to the dictionary learning problem. On one hand, dictionary elements need to be...

Based on the content dual-dictionary learning and sparse representation, we put forward a novel method of image restoration. This method can improve the adaptive ability of the image. To restore the image, the dual-dictionary is trained with sparse representation. Comparing with the traditional dictionary learning algorithm, the method in this paper can capture more high-frequency information a...

While recent supervised dictionary learning methods have attained promising results on the classification tasks, their performance depends on the availability of the large labeled datasets. However, in many real world applications, accessing to sufficient labeled data may be expensive and/or time consuming, but its relatively easy to acquire a large amount of unlabeled data. In this paper, we p...

In this paper we propose a multi-task linear classifier learning problem called D-SVM (Dictionary SVM). D-SVM uses a dictionary of parameter covariance shared by all tasks to do multi-task knowledge transfer among different tasks. We formally define the learning problem of D-SVM and show two interpretations of this problem, from both the probabilistic and kernel perspectives. From the probabili...

Sparse models in dictionary learning have been successfully applied in a wide variety of machine learning and computer vision problems, and as a result have recently attracted increased research interest. Another interesting related problem based on linear equality constraints, namely the sparse null space (SNS) problem, first appeared in 1986 and has since inspired results on sparse basis purs...

While recent techniques for discriminative dictionary learning have demonstrated tremendous success in image analysis applications, their performance is often limited by the amount of labeled data available for training. Even though labeling images is difficult, it is relatively easy to collect unlabeled images either by querying the web or from public datasets. Using the kernel method, we prop...

Sparse Dictionary Learning has recently become popular for discovering latent components that can be used to reconstruct elements in a dataset. Analysis of sequence data could also bene t from this type of decomposition, but sequence datasets are not natively accepted by the Sparse Dictionary Learning model. A strategy for making sequence data more manageable is to extract all subsequences of a...

In this paper, we aim at learning simultaneously a discriminative dictionary and a robust projection matrix from noisy data. The joint learning, makes the learned projection and dictionary a better fit for each other, so a more accurate classification can be obtained. However, current prevailing joint dimensionality reduction and dictionary learning methods, would fail when the training samples...