Intelligent Computing Theories and Application

A novel approach for face recognition via domain adaptation and manifold distance metric learning is presented in this paper. Recently, unconstrained face recognition is becoming a research hot in computer vision. For the non-independent and identically distributed data set, the maximum mean discrepancy algorithm in domain adaption learning is used to represent the difference between the training set and the test set. At the same time, assume that the same type of face data are distributed on the same manifold and the different types of face data are distributed on different manifolds, the face image set is used to model multiple manifolds and the distance between affine hulls is used to represent the distance between manifolds. At last, a projection matrix will be explored by maximizing the distance between manifolds and minimizing the difference between the training set and test set. A large number of experimental results on different face data sets show the efficiency of the proposed method.

[1]  E. Elinav,et al.  Metabolites: messengers between the microbiota and the immune system , 2016, Genes & development.

[2]  Ernesto Estrada.  Virtual identification of essential proteins within the protein interaction network of yeast , 2005, Proteomics.

[3]  De-Shuang Huang,et al.  Normalized Feature Vectors: A Novel Alignment-Free Sequence Comparison Method Based on the Numbers of Adjacent Amino Acids , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[4]  S. Bingham,et al.  Digestion and physiological properties of resistant starch in the human large bowel , 1996, British Journal of Nutrition.

[5]  Xingming Zhao,et al.  Predicting protein–protein interactions from protein sequences using meta predictor , 2010, Amino Acids.

[6]  Marcus J. Claesson,et al.  Genome-scale analyses of health-promoting bacteria: probiogenomics , 2009, Nature Reviews Microbiology.

[7]  Zhen Wang,et al.  SFAPS: An R package for structure/function analysis of protein sequences based on informational spectrum method , 2013, 2013 IEEE International Conference on Bioinformatics and Biomedicine.

[8]  De-Shuang Huang,et al.  A General CPL-AdS Methodology for Fixing Dynamic Parameters in Dual Environments , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  De-Shuang Huang,et al.  Mining the bladder cancer-associated genes by an integrated strategy for the construction and analysis of differential co-expression networks , 2015, BMC Genomics.

[10]  F. Guarner,et al.  Gut flora in health and disease , 2003, The Lancet.

[11]  Zhu-Hong You,et al.  A novel approach based on KATZ measure to predict associations of human microbiota with non‐infectious diseases , 2016, Bioinform..

[12]  De-Shuang Huang,et al.  Predicting Hub Genes Associated with Cervical Cancer through Gene Co-Expression Networks , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[13]  Hon Wai Leong,et al.  Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology , 2010, BMC Bioinformatics.

[14]  F. Bäckhed,et al.  The gut microbiota — masters of host development and physiology , 2013, Nature Reviews Microbiology.

[15]  Xing Chen,et al.  Novel human lncRNA-disease association inference based on lncRNA expression profiles , 2013, Bioinform..

[16]  De-Shuang Huang,et al.  A Two-Stage Geometric Method for Pruning Unreliable Links in Protein-Protein Networks , 2015, IEEE Transactions on NanoBioscience.

[17]  G. Macfarlane,et al.  Colonic microflora: nutrition and health. , 1997, Nutrition.

[18]  A. Stefka,et al.  Review article: fungal microbiota and digestive diseases , 2014, Alimentary pharmacology & therapeutics.

[19]  J. Gilbert,et al.  Microbial metagenomics: beyond the genome. , 2011, Annual review of marine science.

[20]  S. Lebeer,et al.  Lactobacillus species as biomarkers and agents that can promote various aspects of vaginal health , 2015, Front. Physiol..

[21]  Zhu-Hong You,et al.  t-LSE: A Novel Robust Geometric Approach for Modeling Protein-Protein Interaction Networks , 2013, PloS one.

[22]  Qionghai Dai,et al.  WBSMDA: Within and Between Score for MiRNA-Disease Association prediction , 2016, Scientific Reports.

[23]  De-Shuang Huang,et al.  Independent component analysis-based penalized discriminant method for tumor classification using gene expression data , 2006, Bioinform..

[24]  Katherine H. Huang,et al.  Structure, Function and Diversity of the Healthy Human Microbiome , 2012, Nature.

[25]  Xing Chen,et al.  Semi-supervised learning for potential human microRNA-disease associations inference , 2014, Scientific Reports.

[26]  De-Shuang Huang,et al.  A Constructive Hybrid Structure Optimization Methodology for Radial Basis Probabilistic Neural Networks , 2008, IEEE Transactions on Neural Networks.

[27]  Xing-Ming Zhao,et al.  APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility , 2010, BMC Bioinformatics.

[28]  Lei Zhang,et al.  Tumor Clustering Using Nonnegative Matrix Factorization With Gene Selection , 2009, IEEE Transactions on Information Technology in Biomedicine.

[29]  F. Bäckhed,et al.  Tissue factor and PAR1 promote microbiota-induced intestinal vascular remodelling , 2012, Nature.

[30]  Lei Zhang,et al.  Prediction of protein-protein interactions based on protein-protein correlation using least squares regression. , 2014, Current protein & peptide science.

[31]  J. Gordon,et al.  Honor thy symbionts , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[32]  P. Bork,et al.  A human gut microbial gene catalogue established by metagenomic sequencing , 2010, Nature.

[33]  Christopher J. Woolverton,et al.  Prescott's Microbiology. , 2011 .

[34]  De-Shuang Huang,et al.  ChIP-PIT: Enhancing the Analysis of ChIP-Seq Data Using Convex-Relaxed Pair-Wise Interaction Tensor Decomposition , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.