Search Results - "Jieping Ye"

A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers by Wu, Mingrui, Ye, Jieping

“…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…”
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Generalized Linear Discriminant Analysis: A Unified Framework and Efficient Model Selection by Ji, Shuiwang, Ye, Jieping

“…High-dimensional data are common in many domains, and dimensionality reduction is the key to cope with the curse-of-dimensionality . Linear discriminant…”
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Tensor Completion for Estimating Missing Values in Visual Data by Ji Liu, Musialski, P., Wonka, P., Jieping Ye

“…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…”
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Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization by Hu, Yao, Zhang, Debing, Ye, Jieping, Li, Xuelong, He, Xiaofei

“…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…”
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On Similarity Preserving Feature Selection by Zhao, Zheng, Wang, Lei, Liu, Huan, Ye, Jieping

“…In the literature of feature selection, different criteria have been proposed to evaluate the goodness of features. In our investigation, we notice that a…”
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A two-stage linear discriminant analysis via QR-decomposition by Ye, Jieping, Li, Qi

“…Linear discriminant analysis (LDA) is a well-known method for feature extraction and dimension reduction. It has been used widely in many applications…”
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Canonical Correlation Analysis for Multilabel Classification: A Least-Squares Formulation, Extensions, and Analysis by Sun, Liang, Ji, Shuiwang, Ye, Jieping

“…Canonical Correlation Analysis (CCA) is a well-known technique for finding the correlations between two sets of multidimensional variables. It projects both…”
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Hexagon-Based Convolutional Neural Network for Supply-Demand Forecasting of Ride-Sourcing Services by Ke, Jintao, Yang, Hai, Zheng, Hongyu, Chen, Xiqun, Jia, Yitian, Gong, Pinghua, Ye, Jieping

“…Ride-sourcing services are becoming an increasingly popular transportation mode in cities all over the world. With real-time information from both drivers and…”
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How machine learning informs ride-hailing services: A survey by Liu, Yang, Jia, Ruo, Ye, Jieping, Qu, Xiaobo

“…In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease…”
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DeepSD: Supply-Demand Prediction for Online Car-Hailing Services Using Deep Neural Networks by Dong Wang, Wei Cao, Jian Li, Jieping Ye

“…The online car-hailing service has gained great popularity all over the world. As more passengers and more drivers use the service, it becomes increasingly…”
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Generalized Low Rank Approximations of Matrices by Ye, Jieping

“…The problem of computing low rank approximations of matrices is considered. The novel aspect of our approach is that the low rank approximations are on a…”
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Active Batch Selection via Convex Relaxations with Guaranteed Solution Bounds by Chakraborty, Shayok, Balasubramanian, Vineeth, Qian Sun, Panchanathan, Sethuraman, Jieping Ye

“…Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of…”
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Road Traffic Speed Prediction: A Probabilistic Model Fusing Multi-Source Data by Lin, Lu, Li, Jianxin, Chen, Feng, Ye, Jieping, Huai, Jinpeng

“…Road traffic speed prediction is a challenging problem in intelligent transportation system (ITS) and has gained increasing attentions. Existing works are…”
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Modeling disease progression via multi-task learning by Zhou, Jiayu, Liu, Jun, Narayan, Vaibhav A., Ye, Jieping

“…Alzheimer's disease (AD), the most common type of dementia, is a severe neurodegenerative disorder. Identifying biomarkers that can track the progress of the…”
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Analysis of sampling techniques for imbalanced data: An n=648 ADNI study by Dubey, Rashmi, Zhou, Jiayu, Wang, Yalin, Thompson, Paul M., Ye, Jieping

“…Many neuroimaging applications deal with imbalanced imaging data. For example, in Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the mild…”
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Efficient Methods for Overlapping Group Lasso by Yuan, Lei, Liu, Jun, Ye, Jieping

“…The group Lasso is an extension of the Lasso for feature selection on (predefined) nonoverlapping groups of features. The nonoverlapping group structure limits…”
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Deep convolutional neural networks for annotating gene expression patterns in the mouse brain by Zeng, Tao, Li, Rongjian, Mukkamala, Ravi, Ye, Jieping, Ji, Shuiwang

“…Profiling gene expression in brain structures at various spatial and temporal scales is essential to understanding how genes regulate the development of brain…”
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Partially observable environment estimation with uplift inference for reinforcement learning based recommendation by Shang, Wenjie, Li, Qingyang, Qin, Zhiwei, Yu, Yang, Meng, Yiping, Ye, Jieping

“…Reinforcement learning (RL) aims at searching the best policy model for decision making, and has been shown powerful for sequential recommendations. The…”
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Predictive modeling of treatment resistant depression using data from STARD and an independent clinical study by Nie, Zhi, Vairavan, Srinivasan, Narayan, Vaibhav A, Ye, Jieping, Li, Qingqin S

“…Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major…”
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Persistent Homology in Sparse Regression and Its Application to Brain Morphometry by Chung, Moo K., Hanson, Jamie L., Jieping Ye, Davidson, Richard J., Pollak, Seth D.

“…Sparse systems are usually parameterized by a tuning parameter that determines the sparsity of the system. How to choose the right tuning parameter is a…”
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