A vast body of recent works in the literature have shown that exploring structures beyond data lowrankness can boost the performance of subspace clustering methods such as Low-Rank Representation (LRR). It has also been well recognized that the matrix factorization framework might offer more flexibility on pursuing underlying structures of the data. In this paper, we propose to learn structured...

a r t i c l e i n f o Complex event recognition is the problem of recognizing events in long and unconstrained videos. In this extremely challenging task, concepts have recently shown a promising direction where core low-level events (referred to as concepts) are annotated and modeled using a portion of the training data, then each complex event is described using concept scores, which are feat...

Automatic speech recognition (ASR) is a fascinating area of research towards realizing humanmachine interactions. After more than 30 years of exploitation of Gaussian Mixture Models (GMMs), state-of-the-art systems currently rely on Deep Neural Network (DNN) to estimate class-conditional posterior probabilities. The posterior probabilities are used for acoustic modeling in hidden Markov models ...

Many computer vision algorithms employ subspace models to represent data. The Low-rank representation (LRR) has been successfully applied in subspace clustering for which data are clustered according to their subspace structures. The possibility of extending LRR on Grassmann manifold is explored in this paper. Rather than directly embedding Grassmann manifold into a symmetric matrix space, an e...

Face recognition has attracted great interest due to its importance in many real-world applications. In this paper, we present a novel low-rank sparse representation-based classification (LRSRC) method for robust face recognition. Given a set of test samples, LRSRC seeks the lowest-rank and sparsest representation matrix over all training samples. Since low-rank model can reveal the subspace st...

We analyze and improve low rank representation (LRR), the state-of-the-art algorithm for subspace segmentation of data. We prove that for the noiseless case, the optimization model of LRR has a unique solution, which is the shape interaction matrix (SIM) of the data matrix. So in essence LRR is equivalent to factorization methods. We also prove that the minimum value of the optimization model o...

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...

In recent years, we have witnessed a surge of interest in multi-view representation learning. When facing multiple views that are highly related but sightly different from each other, most existing methods might fail to fully explore information. Additionally, pairwise correlations among often vary drastically, which makes challenging. Therefore, how learn appropriate information is still an op...