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Displaying 1-50 out of 73 total
Linear Projection Methods in Face Recognition under Unconstrained Illuminations: A Comparative Study
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Qi Li, Jieping Ye, Chandra Kambhamettu
Issue Date:July 2004
pp. 474-481
Face recognition under unconstrained illuminations (FR/I) received extensive study because of the existence of illumination subspace. [2] presented a study on the comparison between Principal component analysis (PCA) and subspace Linear Discriminant Analys...
 
A new optimization criterion for generalized discriminant analysis on undersampled problems
Found in: Data Mining, IEEE International Conference on
By Jieping Ye, Ravi Janardan, Cheong Hee Park, Haesun Park
Issue Date:November 2003
pp. 419
A new optimization criterion for discriminant analysis is presented. The new criterion extends the optimization criteria of the classical linear discriminant analysis (LDA) by introducing the pseudo-inverse when the scatter matrices are singular. It is app...
 
Spatial Interest Pixels (SIPs): Useful Low-Level Features of Visual Media Data
Found in: Data Mining, IEEE International Conference on
By Qi Li, Jieping Ye, Chandra Kambhamettu
Issue Date:November 2003
pp. 163
Visual media data such as an image is the raw data representation for many important applications. The biggest challenge in using visual media data comes from the extremely high dimensionality. We present a comparative study on spatial interest pixels (SIP...
 
Pairwise Protein Structure Alignment Based on an Orientation-Independent Representation of the Backbone Geometry
Found in: Tools with Artificial Intelligence, IEEE International Conference on
By Jieping Ye, Ravi Janardan, Songtao Liu
Issue Date:November 2003
pp. 2
Determining structural similarities between proteins is an important problem since it can help identify functional and evolutionary relationships. In this paper, an algorithm is proposed to align two protein structures. Given the protein backbones, the alg...
 
Active Matrix Completion
Found in: 2013 IEEE International Conference on Data Mining (ICDM)
By Shayok Chakraborty,Jiayu Zhou,Vineeth Balasubramanian,Sethuraman Panchanathan,Ian Davidson,Jieping Ye
Issue Date:December 2013
pp. 81-90
Recovering a matrix from a sampling of its entries is a problem of rapidly growing interest and has been studied under the name of matrix completion. It occurs in many areas of engineering and applied science. In most machine learning and data mining appli...
 
Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Yao Hu, Debing Zhang, Jieping Ye, Xuelong Li, Xiaofei He
Issue Date:September 2013
pp. 2117-2130
Recovering a large matrix from a small subset of its entries is a challenging problem arising in many real applications, such as image inpainting and recommender systems. Many existing approaches formulate this problem as a general low-rank matrix approxim...
 
A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Shuai Huang,Jing Li,Jieping Ye,Adam Fleisher,Kewei Chen,Teresa Wu,Eric Reiman,the Alzheimer's Disease Neuroimaging Initiative
Issue Date:June 2013
pp. 1328-1342
Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challen...
 
A Convex Formulation for Learning a Shared Predictive Structure from Multiple Tasks
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Jianhui Chen, Lei Tang, Jun Liu, Jieping Ye
Issue Date:May 2013
pp. 1025-1038
In this paper, we consider the problem of learning from multiple related tasks for improved generalization performance by extracting their shared structures. The alternating structure optimization (ASO) algorithm, which couples all tasks using a shared fea...
 
On Similarity Preserving Feature Selection
Found in: IEEE Transactions on Knowledge and Data Engineering
By Zheng Zhao,Lei Wang,Huan Liu,Jieping Ye
Issue Date:March 2013
pp. 619-632
In the literature of feature selection, different criteria have been proposed to evaluate the goodness of features. In our investigation, we notice that a number of existing selection criteria implicitly select features that preserve sample similarity, and...
 
Tensor Completion for Estimating Missing Values in Visual Data
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ji Liu,P. Musialski,P. Wonka, Jieping Ye
Issue Date:January 2013
pp. 208-220
In this paper, we propose an algorithm to estimate missing values in tensors of visual data. The values can be missing due to problems in the acquisition process or because the user manually identified unwanted outliers. Our algorithm works even with a sma...
 
Perspective Analysis for Online Debates
Found in: 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
By S. Tikves,S. Gokalp,M. Temkit,S. Banerjee, Jieping Ye,H. Davulcu
Issue Date:August 2012
pp. 898-905
Internet and social media devices created a new public space for online debate on political and social topics. A debate is defined as a formal discussion on a set of related topics in a public meeting, in which opposing perspectives and arguments are put f...
 
Matrix completion by Truncated Nuclear Norm Regularization
Found in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Debing Zhang, Yao Hu, Jieping Ye, Xuelong Li, Xiaofei He
Issue Date:June 2012
pp. 2192-2199
Estimating missing values in visual data is a challenging problem in computer vision, which can be considered as a low rank matrix approximation problem. Most of the recent studies use the nuclear norm as a convex relaxation of the rank operator. However, ...
 
Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Ying-Xin Li,Shuiwang Ji,Sudhir Kumar,Jieping Ye,Zhi-Hua Zhou
Issue Date:January 2012
pp. 98-112
In the studies of Drosophila embryogenesis, a large number of two-dimensional digital images of gene expression patterns have been produced to build an atlas of spatio-temporal gene expression dynamics across developmental time. Gene expressions captured i...
 
Machine Learning Approaches for the Neuroimaging Study of Alzheimer's Disease
Found in: Computer
By Jieping Ye,Teresa Wu, Jing Li, Kewei Chen
Issue Date:April 2011
pp. 99-101
Machine learning tools aid many Alzheimer's disease-related investigations by enabling multisource data fusion and biomarker identification as well as analysis of functional brain connectivity.
 
Kernel Learning for Efficiency Maximization in the Conformal Predictions Framework
Found in: Machine Learning and Applications, Fourth International Conference on
By Vineeth Balasubramanian, Shayok Chakraborty, Sethuraman Panchanathan, Jieping Ye
Issue Date:December 2010
pp. 235-242
The Conformal Predictions framework is a recent development in machine learning to associate reliable measures of confidence with results in classification and regression. This framework is founded on the principles of algorithmic randomness (Kolmogorov co...
 
Canonical Correlation Analysis for Multilabel Classification: A Least-Squares Formulation, Extensions, and Analysis
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Liang Sun, Shuiwang Ji, Jieping Ye
Issue Date:January 2011
pp. 194-200
Canonical Correlation Analysis (CCA) is a well-known technique for finding the correlations between two sets of multidimensional variables. It projects both sets of variables onto a lower-dimensional space in which they are maximally correlated. CCA is com...
 
A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Mingrui Wu, Jieping Ye
Issue Date:November 2009
pp. 2088-2092
We present a small sphere and large margin approach for novelty detection problems, where the majority of training data are normal examples. In addition, the training data also contain a small number of abnormal examples or outliers. The basic idea is to c...
 
Compressed Facade Displacement Maps
Found in: IEEE Transactions on Visualization and Computer Graphics
By Saif Ali, Jieping Ye, Anshuman Razdan, Peter Wonka
Issue Date:March 2009
pp. 262-273
We describe an approach to render massive urban models. To prevent a memory transfer bottleneck we propose to render the models from a compressed representation directly. Our solution is based on rendering crude building outlines as polygons and generating...
 
A unified framework for generalized Linear Discriminant Analysis
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Shuiwang Ji, Jieping Ye
Issue Date:June 2008
pp. 1-7
Linear Discriminant Analysis (LDA) is one of the well-known methods for supervised dimensionality reduction. Over the years, many LDA-based algorithms have been developed to cope with the curse of dimensionality. In essence, most of these algorithms employ...
 
Kernel Uncorrelated and Regularized Discriminant Analysis: A Theoretical and Computational Study
Found in: IEEE Transactions on Knowledge and Data Engineering
By Shuiwang Ji, Jieping Ye
Issue Date:October 2008
pp. 1311-1321
Linear and kernel discriminant analysis are popular approaches for supervised dimensionality reduction. Uncorrelated and regularized discriminant analysis have been proposed to overcome the singularity problem encountered by classical discriminant analysis...
 
Adaptive Distance Metric Learning for Clustering
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Jieping Ye, Zheng Zhao, Huan Liu
Issue Date:June 2007
pp. 1-7
A good distance metric is crucial for unsupervised learning from high-dimensional data. To learn a metric without any constraint or class label information, most unsupervised metric learning algorithms appeal to projecting observed data onto a low-dimensio...
 
Biased Manifold Embedding: A Framework for Person-Independent Head Pose Estimation
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Vineeth Nallure Balasubramanian, Jieping Ye, Sethuraman Panchanathan
Issue Date:June 2007
pp. 1-7
The estimation of head pose angle from face images is an integral component of face recognition systems, human computer interfaces and other human-centered computing applications. To determine the head pose, face images with varying pose angles can be cons...
 
Integrating Global and Local Structures: A Least Squares Framework for Dimensionality Reduction
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Jianhui Chen, Jieping Ye, Qi Li
Issue Date:June 2007
pp. 1-8
Linear Discriminant Analysis (LDA) is a popular statistical approach for dimensionality reduction. LDA captures the global geometric structure of the data by simultaneously maximizing the between-class distance and minimizing the within-class distance. How...
 
Adaptive Appearance Based Face Recognition
Found in: Tools with Artificial Intelligence, IEEE International Conference on
By Qi Li, Jieping Ye, Min Li, Chandra Kambhamettu
Issue Date:November 2006
pp. 677-684
In this paper, we present an adaptive appearance based face recognition framework that combines the efficiency of global approaches and the robustness of local approaches together. The framework uses a novel eye locator to select an appropriate scheme for ...
 
Feature Reduction via Generalized Uncorrelated Linear Discriminant Analysis
Found in: IEEE Transactions on Knowledge and Data Engineering
By Jieping Ye, Ravi Janardan, Qi Li, Haesun Park
Issue Date:October 2006
pp. 1312-1322
High-dimensional data appear in many applications of data mining, machine learning, and bioinformatics. Feature reduction is commonly applied as a preprocessing step to overcome the curse of dimensionality. Uncorrelated Linear Discriminant Analysis (ULDA) ...
 
Kernel Uncorrelated and Orthogonal Discriminant Analysis: A Unified Approach
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Tao Xiong, Jieping Ye, Vladimir Cherkassky
Issue Date:June 2006
pp. 125-131
Several kernel algorithms have recently been proposed for nonlinear discriminant analysis. However, these methods mainly address the singularity problem in the high dimensional feature space. Less attention has been focused on the properties of the resulti...
 
Integrating Spatial and Discriminant Strength for Feature Selection and Linear Dimensionality Reduction
Found in: Computer Vision and Pattern Recognition Workshop
By Qi Li, Chandra Kambhamettu, Jieping Ye
Issue Date:June 2006
pp. 21
Interest strength assignment to image points is important for selecting good features. Strength assignments using spatial information aim to detect interest points repeatable across different image/illumination transformations, and have been widely adopted...
 
IDR/QR: An Incremental Dimension Reduction Algorithm via QR Decomposition
Found in: IEEE Transactions on Knowledge and Data Engineering
By Jieping Ye, Qi Li, Hui Xiong, Haesun Park, Ravi Janardan, Vipin Kumar
Issue Date:September 2005
pp. 1208-1222
Dimension reduction is a critical data preprocessing step for many database and data mining applications, such as efficient storage and retrieval of high-dimensional data. In the literature, a well-known dimension reduction algorithm is Linear Discriminant...
 
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Jieping Ye, Qi Li
Issue Date:June 2005
pp. 929-941
Linear Discriminant Analysis (LDA) is a well-known method for feature extraction and dimension reduction. It has been used widely in many applications involving high-dimensional data, such as image and text classification. An intrinsic limitation of classi...
 
Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Jieping Ye, Tao Li, Tao Xiong, Ravi Janardan
Issue Date:October 2004
pp. 181-190
<p><b>Abstract</b>—The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high (in the thous...
 
Developmental stage annotation of Drosophila gene expression pattern images via an entire solution path for LDA
Found in: ACM Transactions on Knowledge Discovery from Data (TKDD)
By Jianhui Chen, Jieping Ye, Jieping Ye, Ravi Janardan, Ravi Janardan, Sudhir Kumar, Sudhir Kumar
Issue Date:March 2008
pp. 1-21
Gene expression in a developing embryo occurs in particular cells (spatial patterns) in a time-specific manner (temporal patterns), which leads to the differentiation of cell fates. Images of a Drosophila melanogaster embryo at a given developmental stage,...
     
A Reconstruction Error Based Framework for Multi-label and Multi-view Learning
Found in: IEEE Transactions on Knowledge and Data Engineering
By Buyue Qian,Xiang Wang,Jieping Ye,Ian Davidson
Issue Date:August 2014
pp. 1
A significant challenge to make learning techniques more suitable for general purpose use is to move beyond i) complete supervision, ii) low dimensional data, iii) a single label and single view per instance. Solving these challenges allows working with co...
 
Efficient Methods for Overlapping Group Lasso
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Lei Yuan, Jun Liu, Jieping Ye
Issue Date:September 2013
pp. 2104-2116
The group Lasso is an extension of the Lasso for feature selection on (predefined) nonoverlapping groups of features. The nonoverlapping group structure limits its applicability in practice. There have been several recent attempts to study a more general f...
 
Batch Mode Active Sampling Based on Marginal Probability Distribution Matching
Found in: ACM Transactions on Knowledge Discovery from Data (TKDD)
By Ian Davidson, Jieping Ye, Rita Chattopadhyay, Sethuraman Panchanathan, Wei Fan, Zheng Wang
Issue Date:September 2013
pp. 1-25
Active Learning is a machine learning and data mining technique that selects the most informative samples for labeling and uses them as training data; it is especially useful when there are large amount of unlabeled data and labeling them is expensive. Rec...
     
Multisource domain adaptation and its application to early detection of fatigue
Found in: ACM Transactions on Knowledge Discovery from Data (TKDD)
By Ian Davidson, Jieping Ye, Qian Sun, Rita Chattopadhyay, Sethuraman Panchanathan, Wei Fan
Issue Date:December 2012
pp. 1-26
We consider the characterization of muscle fatigue through a noninvasive sensing mechanism such as Surface ElectroMyoGraphy (SEMG). While changes in the properties of SEMG signals with respect to muscle fatigue have been reported in the literature, the lar...
     
Multi-source learning for joint analysis of incomplete multi-modality neuroimaging data
Found in: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12)
By Jieping Ye, Lei Yuan, Paul M. Thompson, Vaibhav A. Narayan, Yalin Wang
Issue Date:August 2012
pp. 1149-1157
Incomplete data present serious problems when integrating large-scale brain imaging data sets from different imaging modalities. In the Alzheimer's Disease Neuroimaging Initiative (ADNI), for example, over half of the subjects lack cerebrospinal fluid (CSF...
     
Modeling disease progression via fused sparse group lasso
Found in: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12)
By Jiayu Zhou, Jieping Ye, Jun Liu, Vaibhav A. Narayan
Issue Date:August 2012
pp. 1095-1103
Alzheimer's Disease (AD) is the most common neurodegenerative disorder associated with aging. Understanding how the disease progresses and identifying related pathological biomarkers for the progression is of primary importance in Alzheimer's disease resea...
     
Feature grouping and selection over an undirected graph
Found in: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12)
By Jieping Ye, Lei Yuan, Peter Wonka, Sen Yang, Xiaotong Shen, Ying-Cheng Lai
Issue Date:August 2012
pp. 922-930
High-dimensional regression/classification continues to be an important and challenging problem, especially when features are highly correlated. Feature selection, combined with additional structure information on the features has been considered to be pro...
     
Robust multi-task feature learning
Found in: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12)
By Changshui Zhang, Jieping Ye, Pinghua Gong
Issue Date:August 2012
pp. 895-903
Multi-task learning (MTL) aims to improve the performance of multiple related tasks by exploiting the intrinsic relationships among them. Recently, multi-task feature learning algorithms have received increasing attention and they have been successfully ap...
     
Batch mode active sampling based on marginal probability distribution matching
Found in: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12)
By Ian Davidson, Jieping Ye, Rita Chattopadhyay, Sethuraman Panchanathan, Wei Fan, Zheng Wang
Issue Date:August 2012
pp. 741-749
Active Learning is a machine learning and data mining technique that selects the most informative samples for labeling and uses them as training data; it is especially useful when there are large amount of unlabeled data and labeling them is expensive. Rec...
     
Optimal exact least squares rank minimization
Found in: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12)
By Jieping Ye, Shuo Xiang, Xiaotong Shen, Yunzhang Zhu
Issue Date:August 2012
pp. 480-488
In multivariate analysis, rank minimization emerges when a low-rank structure of matrices is desired as well as a small estimation error. Rank minimization is nonconvex and generally NP-hard, imposing one major challenge. In this paper, we consider a nonco...
     
Accelerated singular value thresholding for matrix completion
Found in: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12)
By Debing Zhang, Jieping Ye, Jun Liu, Xiaofei He, Yao Hu
Issue Date:August 2012
pp. 298-306
Recovering a large matrix from a small subset of its entries is a challenging problem arising in many real world applications, such as recommender system and image in-painting. These problems can be formulated as a general matrix completion problem. The Si...
     
Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks
Found in: ACM Transactions on Knowledge Discovery from Data (TKDD)
By Jianhui Chen, Jieping Ye, Ji Liu
Issue Date:February 2012
pp. 1-31
We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multitask learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term an...
     
Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Jieping Ye, Shuiwang Ji, Sudhir Kumar, Ying-Xin Li, Zhi-Hua Zhou
Issue Date:January 2012
pp. 98-112
In the studies of Drosophila embryogenesis, a large number of two-dimensional digital images of gene expression patterns have been produced to build an atlas of spatio-temporal gene expression dynamics across developmental time. Gene expressions captured i...
     
Learning incoherent sparse and low-rank patterns from multiple tasks
Found in: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '10)
By Ji Liu, Jianhui Chen, Jieping Ye
Issue Date:July 2010
pp. 1179-1188
We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term a...
     
An efficient algorithm for a class of fused lasso problems
Found in: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '10)
By Jieping Ye, Jun Liu, Lei Yuan
Issue Date:July 2010
pp. 323-332
The fused Lasso penalty enforces sparsity in both the coefficients and their successive differences, which is desirable for applications with features ordered in some meaningful way. The resulting problem is, however, challenging to solve, as the fused Las...
     
A scalable two-stage approach for a class of dimensionality reduction techniques
Found in: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '10)
By Betul Ceran, Jieping Ye, Liang Sun
Issue Date:July 2010
pp. 313-322
Dimensionality reduction plays an important role in many data mining applications involving high-dimensional data. Many existing dimensionality reduction techniques can be formulated as a generalized eigenvalue problem, which does not scale to large-size p...
     
Mining brain region connectivity for alzheimer's disease study via sparse inverse covariance estimation
Found in: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '09)
By Eric Reiman, Jieping Ye, Jing Li, Jun Liu, Kewei Chen, Liang Sun, Rinkal Patel, Teresa Wu
Issue Date:June 2009
pp. 1-24
Effective diagnosis of Alzheimer's disease (AD), the most common type of dementia in elderly patients, is of primary importance in biomedical research. Recent studies have demonstrated that AD is closely related to the structure change of the brain network...
     
Mining discrete patterns via binary matrix factorization
Found in: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '09)
By Bao-Hong Shen, Jieping Ye, Shuiwang Ji
Issue Date:June 2009
pp. 1-24
Mining discrete patterns in binary data is important for subsampling, compression, and clustering. We consider rank-one binary matrix approximations that identify the dominant patterns of the data, while preserving its discrete property. A best approximati...
     
Large-scale sparse logistic regression
Found in: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '09)
By Jianhui Chen, Jieping Ye, Jun Liu
Issue Date:June 2009
pp. 1-24
Logistic Regression is a well-known classification method that has been used widely in many applications of data mining, machine learning, computer vision, and bioinformatics. Sparse logistic regression embeds feature selection in the classification framew...
     
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