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Studies on nowadays human-machine interface have demonstrated that visual information can enhance speech recognition accuracy especially in noisy environments. Deep learning has been widely used to tackle such audio visual speech recognition (AVSR) problem due to its astonishing achievements in both speech recognition and image recognition. Although existing deep learning models succeed to incorporate...
Audio matching automatically retrieves all excerpts that have the same content as the query audio clip from given audio recordings. The extracted feature is critical for audio matching and the Chroma Energy Normalized Statistics (CENS) feature is the state-of-the-arts. However, CENS might behave unsatisfactorily on some audio because it is a handcraft feature. In this paper, we propose to utilize...
Kernel independent component analysis (KICA) detects primary independent components of data by minimizing kernelized canonical correlation of random variables in a reproducing kernel Hilbert space. KICA has been widely used in many practical tasks, e.g., blind source separation and speech recognition. However, the dense kernel matrix in traditional KICA causes high computational complexity which prohibits...
Graph regularized non-negative matrix factorization (GNMF) decomposes a high-dimensional non-negative data matrix into two low-dimensional matrices with the non-negativity property kept and the geometric structure preserved. Due to its effectiveness, GNMF has been widely used in many fields such as computer vision and data mining. However, GNMF cannot process large-scale datasets on distributed system...
Non-negative matrix factorization (NMF) has been widely used to reduce dimensionality of data in image processing and various applications. Incorporating the geometric structure into NMF, graph regularized non-negative matrix factorization (GNMF) has shown significant performance improvement in comparison to conventional NMF. However, both NMF and GNMF require the data matrix to reside in the memory,...
Wearable device with an ego-centric camera would be the next generation device for human-computer interaction such as robot control. Hand gesture is a natural way of ego-centric human-computer interaction. In this paper, we present an ego-centric multi-stage hand gesture analysis pipeline for robot control which works robustly in the unconstrained environment with varying luminance. In particular,...
Genome-wide expression data consists of millions of measurements towards large number of genes, and thus it is challenging for human beings to directly analyze such large-scale data. Clustering provides a more convenient way to analyze gene expression data because it can subdivide raw data into comprehensive classes. However, the number of probed genes is rather greater than the number of samples,...
Background Drug repositioning, finding new indications for existing drugs, has gained much recent attention as a potentially efficient and economical strategy for accelerating new therapies into the clinic. Although improvement in the sensitivity of computational drug repositioning methods has identified numerous credible repositioning opportunities, few have been progressed. Arguably the “black box”...
Nonnegative matrix factorization (NMF) is an effective speech separation approach of extracting discriminative components of different speaker. However, traditional NMF focuses only on the additive combination of the components and ignores the dependencies of speeches. Convolutive NMF (CNMF) captures the dependencies of speeches by overlapping components and achieves better separation performance...
Non-negative matrix factorization (NMF) decomposes a group of non-negative examples into both lower-rank factors including the basis and coefficients. It still suffers from the following deficiencies: 1) it does not always ensure the decomposed factors to be sparse theoretically, and 2) the learned basis often stays away from original examples, and thus lacks enough representative capacity. This paper...
This paper formulates multi-label learning as a constrained projective non-negative matrix factorization (CPNMF) problem which concentrates on a variant of the original projective NMF (PNMF) and explicitly introduces an auxiliary basis to learn the semantic subspace and boosts its discriminating ability by exploiting labeled and unlabeled examples together. Particularly, it propagates labels of the...
Non-negative matrix factorization (NMF) decomposes any non-negative matrix into the product of two low dimensional non-negative matrices. Since NMF learns effective parts-based representation, it has been widely applied in computer vision and data mining. However, traditional NMF has the risk learning rank-deficient basis on high-dimensional dataset with few examples especially when some examples...
Nonnegative matrix factorization (NMF) is a powerful technique for dimensionality reduction. Conventional NMF algorithms usually keep the matrices W and H nonnegative while iterating. However, to get the NMF of a matrix, it's unnecessary to force the temporary solutions in iterations nonnegative. In this paper, we propose a two-staged approach for NMF. At the relaxation stage, the nonnegative constraint...
Sparse coding has shown its great potential in learning image feature representation. Recent developed methods such as group sparse coding prefer discovering the group relationships among examples and have achieved the state-of-the-art results in image classification. However, they suffer from poor robustness shortcomings in practice. This paper proposes a robust weighted supervised sparse coding...
Non-negative matric factorization (NMF) decomposes a given data matrix X into the product of two lower dimensional non-negative matrices U and V. It has been widely applied in pattern recognition and computer vision because of its simplicity and effectiveness. However, existing NMF methods often fail to learn the sparse representation on high-dimensional dataset, especially when some examples are...
Partitioning links rather than nodes is effective in overlapping community detection (OCD) on complex networks. However, it consumes high CPU and memory overheads because the volume of links is huge especially when the network is rather complex. In this paper, we proposes a symmetric non-negative matrix factorization (SNMF) based link partition method called SNMF-Link to overcome this deficiency....
Non-negative matrix factorization (NMF) is a powerful dimension reduction method and has been widely used in many pattern recognition and computer vision problems. However, conventional NMF methods are neither robust enough as their loss functions are sensitive to outliers, nor discriminative because they completely ignore labels in a dataset. In this paper, we proposed a correntropy supervised NMF...
Modern understanding of microbiology largely lays foundation in the biological characterization of microorganisms. However, the landscape relationships of host transcriptional response (HTR) to different bacterial pathogens have not yet been systematically explored. Here, we established the first generation of HTR network (HTRN) according to the HTR similarities among 21 different human pathogenic...
Semi-supervised learning (SSL) utilizes plenty of unlabeled examples to boost the performance of learning from limited labeled examples. Due to its great discriminant power, SSL has been widely applied to various real-world tasks such as information retrieval, pattern recognition, and speech separa- tion. Label propagation (LP) is a popular SSL method which propagates labels through the dataset along...
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