In recent years, more and more researchers' attention has been drawn to the sparse representation-based classification (SRC) method and its application in image analysis and pattern recognition, due to its good characteristics of high recognition rate, robustness to corruption and occlusion, and little dependence on the features selection etc. However, sufficient training samples are always req...

We describe improvements made over the past year to Joshua, an open-source translation system for parsing-based machine translation. The main contributions this past year are significant improvements in both speed and usability of the grammar extraction and decoding steps. We have also rewritten the decoder to use a sparse feature representation, enabling training of large numbers of features w...

With the aid of a universal facial variation dictionary, sparse representation based classifier (SRC) has been naturally extended for face recognition (FR) with single sample per person (SSPP) and achieved promising performance. However, extracting discriminative facial features and building powerful representation framework for classifying query face images are still the bottlenecks of improvi...

We present a probabilistic modeling and inference framework for discriminative analysis dictionary learning under a weak supervision setting. Dictionary learning approaches have been widely used for tasks such as low-level signal denoising and restoration as well as high-level classification tasks, which can be applied to audio and image analysis. Synthesis dictionary learning aims at jointly l...

Constructing an informative and discriminative graph plays an important role in various pattern recognition tasks such as clustering and classification. Among the existing graph-based learning models, low-rank representation (LRR) is a very competitive one, which has been extensively employed in spectral clustering and semi-supervised learning (SSL). In SSL, the graph is composed of both labele...

In this paper, we propose a novel sparse representation based framework for classifying complicated human gestures captured as multi-variate time series (MTS). The novel feature extraction strategy, CovSVDK, can overcome the problem of inconsistent lengths among MTS data and is robust to the large variability within human gestures. Compared with PCA and LDA, the CovSVDK features are more effect...

Support Vector Data Description (SVDD) methods have been successfully applied to tasks such as hyperspectral anomaly detection [1] and spectral unmixing [2] [3]. Unfortunately, the performance of SVDD methods suffers when noisy or non-informative bands are present in the data. If a set of sparse bands could be identified for these techniques, the resulting data may improve SVDD performance whil...