The Violent Scenes Detection Task of MediaEval provides a valuable platform for algorithm evaluation and performance comparison. This is a very challenging task as there exist many forms of violent scenes, which vary significantly in their visual and auditory clues. In this notebook paper, we describe our system used in MediaEval 2013, which focuses on the… (More)
This paper studies the recovery guarantees of the models of minimizing X * + 1 2α X 2 F where X is a tensor and X * and X F are the trace and Frobenius norm of respectively. We show that they can efficiently recover low-rank tensors. In particular, they enjoy exact guarantees similar to those known for minimizing X * under the conditions on the sensing… (More)
In this paper we propose an algorithm to classify tensor data. Our methodology is built on recent studies about matrix classification with the trace norm constrained weight matrix and the tensor trace norm. Similar to matrix classification, the tensor classification is formulated as a convex optimization problem which can be solved by using the… (More)
In this article, a novel framework based on trace norm minimization for audio classification is proposed. In this framework, both the feature extraction and classification are obtained by solving corresponding convex optimization problem with trace norm regularization. For feature extraction, robust principle component analysis (robust PCA) via minimization… (More)
Mixture models have been widely used in modeling of continuous observations. For the possibility to estimate the parameters of a mixture model consistently on the basis of observations from the mixture, identifiability is a necessary condition. In this study, we give some results on the identifiability of multivariate logistic mixture models.
In this paper, a novel framework based on trace norm minimization for audio segment is proposed. In this framework, both the feature extraction and classification are obtained by solving corresponding convex optimization problem with trace norm regularization. For feature extraction, robust principle component analysis (robust PCA) via minimization a… (More)
PROXTONE is a novel and fast method for optimization of large scale non-smooth convex problem . In this work, we try to use PROXTONE method in solving large scale non-smooth non-convex problems, for example training of sparse deep neural network (sparse DNN) or sparse convolutional neural network (sparse CNN) for embedded or mobile device. PROXTONE… (More)