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| 1 | Graph Regularized L_p Smooth Non-negative Matrix Factorization for Data Representation显示文摘This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information of a data set and produces smooth and stable solutions. The main contributions are as follows: first, graph regularization is added into NMF to discover the hidden semantics and simultaneously respect the intrinsic geometric structure information of a data set. Second,the Lpsmoothing constraint is incorporated into NMF to combine the merits of isotropic(L_2-norm) and anisotropic(L_1-norm)diffusion smoothing, and produces a smooth and more accurate solution to the optimization problem. Finally, the update rules and proof of convergence of GSNMF are given. Experiments on several data sets show that the proposed method outperforms related state-of-the-art methods. | Chengcai Leng Hai Zhang Guorong Cai Irene Cheng Anup Basu | 2019 | IEEE/CAA Journal of Automatica Sinica2019，6，2： | 9 |
| 2 | Total Variation Constrained Non-Negative Matrix Factorization for Medical Image Registration显示文摘This paper presents a novel medical image registration algorithm named total variation constrained graphregularization for non-negative matrix factorization(TV-GNMF).The method utilizes non-negative matrix factorization by total variation constraint and graph regularization.The main contributions of our work are the following.First,total variation is incorporated into NMF to control the diffusion speed.The purpose is to denoise in smooth regions and preserve features or details of the data in edge regions by using a diffusion coefficient based on gradient information.Second,we add graph regularization into NMF to reveal intrinsic geometry and structure information of features to enhance the discrimination power.Third,the multiplicative update rules and proof of convergence of the TV-GNMF algorithm are given.Experiments conducted on datasets show that the proposed TV-GNMF method outperforms other state-of-the-art algorithms. | Chengcai Leng Hai Zhang Guorong Cai Zhen Chen Anup Basu | 2021 | IEEE/CAA Journal of Automatica Sinica2021，8，5： | 3 |
| 3 | An experimental and theoretical investigation on torrefaction of a large wet wood particle显示文摘 | Prabir Basu Anup Kumar Sadhukhan Parthapratim Gupta | 2014 | Bioresource Technology2014，159，2： | 1 |
| 4 | Scalable edge enhancement with automatic optimization for digital radiographic images显示文摘 | Lijun Yin Anup Basu Ja Kwei Chang | 2004 | Pattern Recognition2004，37，： | 1 |
| 5 | Deletions in chromosome 4 differentially associated with the development of cervical cancer: evidence of slit2 as a candidate tumor suppressor gene显示文摘 | Ratnesh Kumar Singh Dipanjana Indra Sraboni Mitra Ranajit Kumar Mondal Partha Sarathi Basu Anup Roy Susanta Roychowdhury Chinmay Kumar Panda | 2007 | Human Genetics2007，，1： | 1 |
| 6 | Active camera calibration using pan,tilt and roll显示文摘 | Anup BASU Kavita RAVI | 1997 | IEEE Transactions on Systems Man and Cybernetics1997，27，3： | 1 |
| 7 | lmproving Fractal Codes Based Image Retrieval Using Histogram of Collage Errors显示文摘 | Ming Hong Pi Chong Sze Tong Anup Basu | 2003 | SPIE2003，27，： | 1 |
| 8 | Scalable edge enhancement with automatic optimization for digital radiographic images显示文摘 | Lijun Yin Anup Basu Ja Kwei Chang | 2004 | Pattern Recognition2004，37，： | 1 |
| 9 | Epidemiology of dengue virus infections in Nepal, 2006-2019显示文摘Background:Dengue is one of the newest emerging diseases in Nepal with increasing burden and geographic spread over the years.The main objective of this study was to explore the epidemiological patterns of dengue since its first outbreak(2006)to 2019 in Nepal. | Komal Raj Rijal Bipin Adhikari Bindu Ghimire Binod Dhungel Uttam Raj Pyakurel Prakash Shah Anup Bastola Binod Lekhak Megha Raj Banjara Basu Dev Pandey Daniel MParker Prakash Ghimire | 2021 | Infectious Diseases of Poverty2021，10，2： | 0 |
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