作者：
Liu, HuapingGuo, DiSun, FuchunTsinghua Univ
Tsinghua Natl Lab Informat Sci & Technol State Key Lab Intelligent Technol & Syst Dept Comp Sci & Technol Beijing 100084 Peoples R China
Dexterous robots have emerged in the last decade in response to the need for fine-motor-control assistance in applications as diverse as surgery, undersea welding, and mechanical manipulation in space. Crucial to the ...
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Dexterous robots have emerged in the last decade in response to the need for fine-motor-control assistance in applications as diverse as surgery, undersea welding, and mechanical manipulation in space. Crucial to the fine operation and contact environmental perception are tactile sensors that are fixed on the robotic fingertips. These can be used to distinguish material texture, roughness, spatial features, compliance, and friction. In this paper, we regard the investigated tactile data as time sequences, of which dissimilarity can be evaluated by the popular dynamic time warping method. A kernel sparsecoding method is therefore developed to address the tactile data representation and classification problem. However, the naive use of sparsecoding neglects the intrinsic relation between individual fingers, which simultaneously contact the object. To tackle this problem, we develop a joint kernel sparsecoding model to solve the multifinger tactile sequence classification problem. In this model, the intrinsic relations between fingers are explicitly taken into account using the joint sparse coding, which encourages all of the coding vectors to share the same sparsity support pattern. The experimental results show that the joint sparse coding achieves better performance than conventional sparsecoding.
A novel structure which combines the advantages of ratio mask (RM) and joint dictionary learning (JDL) is proposed for single-channel speech enhancement in this paper. The novel speech enhancement structure makes full...
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A novel structure which combines the advantages of ratio mask (RM) and joint dictionary learning (JDL) is proposed for single-channel speech enhancement in this paper. The novel speech enhancement structure makes full use of the training data and overcomes some shortcomings of generative dictionary learning (GDL) algorithm. RMs of speech and interferer are introduced to provide the discriminative information both in the training stage and enhancement stage of the novel structure. In the training stage, the signals and their corresponding ideal RMs (IRMs) are used to learn the signal and IRM dictionaries jointly by K-SVD algorithm. In the enhancement stage, the mixture signal and mixture RM are sparsely represented over the composite dictionaries composed of the learned signal and IRM dictionaries to formulate a joint sparse coding (JSC) problem. Then, the estimated RMs (ERMs) of speech and interferer in the mixture are calculated to develop two soft mask (SM) filters. The proposed SM filters incorporate ideal binary mask technique and Wiener-type filter to make full use of the discriminative information provided by the ERMs. They are used to both strengthen the speech and suppress the interferer in the mixture. The proposed algorithms have shown their abilities to improve both speech intelligibility and quality. Experimental evaluations verify the proposed algorithms obtain comparable performances to a deep neural network (DNN) based mask estimator with lower computation and perform better than other tested algorithms. (C) 2016 Elsevier B.V. All rights reserved.
We propose a data-driven geolocation method on microblog text. Key idea underlying our approach is sparsecoding, an unsupervised learning algorithm. Unlike conventional positioning algorithms, we geolocate a user by ...
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ISBN:
(纸本)9781450334594
We propose a data-driven geolocation method on microblog text. Key idea underlying our approach is sparsecoding, an unsupervised learning algorithm. Unlike conventional positioning algorithms, we geolocate a user by identifying features extracted from her social media text. We also present an enhancement robust to erasure of words in the text and report our experimental results with uniformly or randomly subsampled microblog text. Our solution features a novel two-step procedure consisting of upconversion and iterative refinement by joint sparse coding. As a result, we can reduce the amount of input data required by geolocation while preserving good prediction accuracy. In the light of information preservation and privacy, we remark potential applications of these results.
In this paper, we try to address the joint optimization problem of the extreme learning machines corresponding to different features. The method is based on the L (2,1) norm penalty, which encourages jointsparse codi...
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In this paper, we try to address the joint optimization problem of the extreme learning machines corresponding to different features. The method is based on the L (2,1) norm penalty, which encourages joint sparse coding. By adopting such a technology, the intrinsic relation between different features can be sufficiently preserved. To tackle the problem that the labeled samples is rare, we introduce the semi-supervised regularization term and seamlessly incorporate them into the particle filter framework to realize visual tracking. In addition, an online updating strategy is introduced which also exploits the large amount of unlabeled samples that are collected during the tracking period. Finally, the proposed tracking algorithm is compared to other state-of-the-arts on some challenging video sequences and shows promising results.
To build an effective dimensionality reduction model usually requires sufficient data. Otherwise, traditional dimensionality reduction methods might be less effective. However, sufficient data cannot always be guarant...
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To build an effective dimensionality reduction model usually requires sufficient data. Otherwise, traditional dimensionality reduction methods might be less effective. However, sufficient data cannot always be guaranteed in real applications. In this paper we focus on performing unsupervised dimensionality reduction on the high-dimensional and small-sized data, in which the dimensionality of target data is high and the number of target data is small. To handle the problem, we propose a novel Self-taught Dimensionality Reduction (STDR) approach, which is able to transfer external knowledge (or information) from freely available external (or auxiliary) data to the high-dimensional and small-sized target data. The proposed STDR consists of three steps: First, the bases are learnt from sufficient external data, which might come from the same "type" or "modality" of target data. The bases are the common part between external data and target data, i.e., the external knowledge (or information). Second, target data are reconstructed by the learnt bases by proposing a novel joint graph sparsecoding model, which not only provides robust reconstruction ability but also preserves the local structures amongst target data in the original space. This process transfers the external knowledge (i.e., the learnt bases) to target data. Moreover, the proposed solver to the proposed model is theoretically guaranteed that the objective function of the proposed model converges to the global optimum. After this, target data are mapped into the learnt basis space, and are sparsely represented by the bases, i.e., represented by parts of the bases. Third, the sparse features (that is, the rows with zero (or small) values) of the new representations of target data are deleted for achieving the effectiveness and the efficiency. That is, this step performs feature selection on the new representations of target data. Finally, experimental results at various types of datasets show the proposed STD
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