Yu-Xiong Wang

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Nonnegative Matrix Factorization (NMF), a relatively novel paradigm for dimensionality reduction, has been in the ascendant since its inception. It incorporates the nonnegativity constraint and thus obtains the parts-based representation as well as enhancing the interpretability of the issue correspondingly. This survey paper mainly focuses on the(More)
At present, dictionary based models have been widely used in image classification. The image features are approximated as a linear combination of bases selected from the dictionary in a sparse space, resulting in compact patterns. The features applied to image classification usually reside on low dimensional manifolds embedded in a high dimensional ambient(More)
Traditional sparse representation algorithms usually operate in a single Euclidean space. This paper leverages a self-explanatory reformulation of sparse representation, i.e., linking the learned dictionary atoms with the original feature spaces explicitly, to extend simultaneous dictionary learning and sparse coding into reproducing kernel Hilbert spaces(More)
We develop a conceptually simple but powerful approach that can learn novel categories from few annotated examples. In this approach, the experience with already learned categories is used to facilitate the learning of novel classes. Our insight is two-fold: 1) there exists a generic, category agnostic transformation from models learned from few samples to(More)
Non-negative Matrix Factorization (NMF) has become a powerful tool for image representation due to its enhanced semantic interpretability under non-negativity. Unfortunately, two types of neighborhood information essential to representation are lost in NMF. For individual image, the local structure information is missing in the vectorization, which can then(More)
This paper proposes a novel patch propagation inpainting algorithm based on Weighted Sparse Non-negative Matrix Factorization (WSNMF). Unlike existing methods, we cast the inpainting task as a sequential low-rank matrix recovery and completion problem, where the incomplete data matrix consists of the image patch to be inpainted and several similar intact(More)
Recently, dictionary learned by sparse coding has been widely adopted in image classification and has achieved competitive performance. Sparse coding is capable of reducing the reconstruction error in transforming low-level descriptors into compact mid-level features. Nevertheless, dictionary learned by sparse coding does not have the ability to distinguish(More)
In this paper, we explore an approach to generating detectors that is radically different from the conventional way of learning a detector from a large corpus of annotated positive and negative data samples. Instead, we assume that we have evaluated “off-line” a large library of detectors against a large set of detection tasks. Given a new(More)
Sparse representation based classification (SRC) and collaborative representation based classification (CRC) have demonstrated impressive performance for visual recognition. SRC and CRC assume that the training samples in each class contribute equally to the dictionary and thus generate the dictionary that consists of the training samples in the(More)