Tensor completion for estimating missing values in visual data
- Ji Liu, Przemyslaw Musialski, Peter Wonka, Jieping Ye
- Computer ScienceIEEE International Conference on Computer Vision
- 1 September 2009
An algorithm to estimate missing values in tensors of visual data by laying out the theoretical foundations and building a working algorithm is proposed, which is more accurate and robust than heuristic approaches.
Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization
- Yao Hu, Debing Zhang, Jieping Ye, Xuelong Li, Xiaofei He
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 1 September 2013
This paper proposes to achieve a better approximation to the rank of matrix by truncated nuclear norm, which is given by the nuclear norm subtracted by the sum of the largest few singular values, and develops a novel matrix completion algorithm by minimizing the Truncated Nuclear Norm.
Generalized Low Rank Approximations of Matrices
- Jieping Ye
- Computer ScienceMachine-mediated learning
- 4 July 2004
An iterative algorithm, namely GLRAM, which stands for the Generalized Low Rank Approximations of Matrices is derived, which reduces the reconstruction error sequentially, and the resulting approximation is thus improved during successive iterations.
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
- Huaxiu Yao, Fei Wu, Z. Li
- Computer ScienceAAAI Conference on Artificial Intelligence
- 23 February 2018
A Deep Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial and temporal relations is proposed, which demonstrates effectiveness of the approach over state-of-the-art methods.
Two-Dimensional Linear Discriminant Analysis
- Jieping Ye, Ravi Janardan, Qi Li
- Computer ScienceNIPS
- 1 December 2004
2DLDA, a novel LDA algorithm, which stands for 2-Dimensional Linear Discriminant Analysis, overcomes the singularity problem implicitly, while achieving efficiency and the combination of 2DLDA and classical LDA, namely 2 DLDA+LDA, is studied.
An accelerated gradient method for trace norm minimization
- Shuiwang Ji, Jieping Ye
- Computer ScienceInternational Conference on Machine Learning
- 14 June 2009
This paper exploits the special structure of the trace norm, based on which it is proposed an extended gradient algorithm that converges as O(1/k) and proposes an accelerated gradient algorithm, which achieves the optimal convergence rate of O( 1/k2) for smooth problems.
Characterization of a Family of Algorithms for Generalized Discriminant Analysis on Undersampled Problems
- Jieping Ye
- Computer ScienceJournal of machine learning research
- 1 December 2005
A generalized discriminant analysis based on a new optimization criterion that extends the optimization criteria of the classical Linear Discriminant Analysis (LDA) when the scatter matrices are singular is presented.
A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems
- Pinghua Gong, Changshui Zhang, Zhaosong Lu, Jianhua Z. Huang, Jieping Ye
- Computer ScienceInternational Conference on Machine Learning
- 18 March 2013
A General Iterative Shrinkage and Thresholding (GIST) algorithm to solve the nonconvex optimization problem for a large class of non-conveX penalties and a detailed convergence analysis of the GIST algorithm is presented.
Multi-Task Feature Learning Via Efficient l2, 1-Norm Minimization
- Jun Liu, Shuiwang Ji, Jieping Ye
- Computer ScienceConference on Uncertainty in Artificial…
- 18 June 2009
This paper proposes to accelerate the computation of the l2, 1-norm regularized regression model by reformulating it as two equivalent smooth convex optimization problems which are then solved via the Nesterov's method---an optimal first-order black-box method for smooth conveX optimization.
Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting
- Xu Geng, Yaguang Li, Yan Liu
- Computer ScienceAAAI Conference on Artificial Intelligence
- 17 July 2019
The spatiotemporal multi-graph convolution network (ST-MGCN), a novel deep learning model for ride-hailing demand forecasting, is proposed which first encode the non-Euclidean pair-wise correlations among regions into multiple graphs and then explicitly model these correlations using multi- graph convolution.
...
...