The study of the neuronal correlates of the spontaneous alternation in perception elicited by bistable visual stimuli is promising for understanding the mechanism of neural information processing and the neural basis of visual perception and perceptual decision-making. In this paper, we develop a sparse nonnegative tensor factorization-(NTF)-based method to extract features from the local field...

Zhaohui Xue 1,*, Peijun Du 2,3,4, Hongjun Su 1 and Shaoguang Zhou 1 1 School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China; [email protected] (H.S.); [email protected] (S.Z.) 2 Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China; dupjrs@g...

Abstract: Gender recognition and age detection are important problems in telephone speech processing to investigate the identity of an individual using voice characteristics. In this paper a new gender and age recognition system is introduced based on generative incoherent models learned using sparse non-negative matrix factorization and atom correction post-processing method. Similar to genera...

Texts are the major information carrier for internet users, from which learning latent representations has important research and practical value. Neural topic models have been proposed great performance in extracting interpretable topics of texts. However, there remain two limitations: (1) these methods generally ignore contextual texts limited feature representation ability due to shallow fee...

based on the compressed sensing theory, if a signal is sparse in a suitable space, by using the optimization methods, signal could be accurately reconstructed from measurements that are significantly less than the theoretical shannon requirements. the sparse representation may exist for the signal and it is not available for the noise; this could be used to distinguish these two. on the other h...

With the abundance of video data, the interest in more effective methods for recognizing faces from unconstrained videos has grown. State-of-the-art algorithms for describing an image set use descriptors that are either very high-dimensional and/or sensitive to outliers and image misalignment. In this paper, we represent image sets as dictionaries of Symmetric Positive Definite (SPD) matrices t...

This paper proposes a novel texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and non-rigid deformations. Unlike many existing feature extraction methods, which treat the neighborhood of every pixel as a candidate texture element, the proposed algorithm works by selecting a sparse set of affine-invarian...

In this paper, we consider the common problem of automatically recognizing palmprint with varying illumination and image noise. Gabor wavelets can be well represented for biometric image for their similar characteristics to human visual system. However, these Gabor-based algorithms are not robust for image recognition under non-uniform illumination and noise corruption. To improve the recogniti...

Different from familiar clustering objects, text documents have sparse data spaces. A common way of representing a document is as a bag of its component words, but the semantic relations between words are ignored. In this paper, we propose a novel document representation approach to strengthen the discriminative feature of document objects. We replace terms of documents with concepts in WordNet...