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10/23/2020
Escape saddle points faster on manifolds via perturbed Riemannian stochastic recursive gradient
In this paper, we propose a variant of Riemannian stochastic recursive g...
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08/17/2020
Adaptive Multi-level Hyper-gradient Descent
Adaptive learning rates can lead to faster convergence and better final ...
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08/11/2020
Riemannian stochastic recursive momentum method for non-convex optimization
We propose a stochastic recursive momentum method for Riemannian non-con...
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07/26/2020
Regularized Flexible Activation Function Combinations for Deep Neural Networks
Activation in deep neural networks is fundamental to achieving non-linea...
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07/22/2020
Graph Neural Networks with Haar Transform-Based Convolution and Pooling: A Complete Guide
Graph Neural Networks (GNNs) have recently caught great attention and ac...
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07/03/2020
Variance reduction for Riemannian non-convex optimization with batch size adaptation
Variance reduction techniques are popular in accelerating gradient desce...
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01/23/2020
A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting
Value-at-Risk (VaR) and Expected Shortfall (ES) are widely used in the f...
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01/17/2020
ADAMT: A Stochastic Optimization with Trend Correction Scheme
Adam-type optimizers, as a class of adaptive moment estimation methods w...
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11/15/2019
Coupling Matrix Manifolds and Their Applications in Optimal Transport
Optimal transport (OT) is a powerful tool for measuring the distance bet...
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11/07/2019
Efficient Spatial Nearest Neighbor Queries Based on Multi-layer Voronoi Diagrams
Nearest neighbor (NN) problem is an important scientific problem. The NN...
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11/05/2019
A Heuristic Algorithm Based on Tour Rebuilding Operator for the Traveling Salesman Problem
TSP (Traveling Salesman Problem), a classic NP-complete problem in combi...
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10/19/2019
LSTM-Assisted Evolutionary Self-Expressive Subspace Clustering
Massive volumes of high-dimensional data that evolves over time is conti...
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08/14/2019
Tensor-Train Parameterization for Ultra Dimensionality Reduction
Locality preserving projections (LPP) are a classical dimensionality red...
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07/23/2019
Shared Generative Latent Representation Learning for Multi-view Clustering
Clustering multi-view data has been a fundamental research topic in the ...
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06/10/2019
DataLearner: A Data Mining and Knowledge Discovery Tool for Android Smartphones and Tablets
Smartphones have become the ultimate 'personal' computer, yet despite th...
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02/11/2019
Manifold Optimisation Assisted Gaussian Variational Approximation
Variational approximation methods are a way to approximate the posterior...
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01/29/2019
Sparse Least Squares Low Rank Kernel Machines
A general framework of least squares support vector machine with low ran...
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11/12/2018
A Review for Weighted MinHash Algorithms
Data similarity (or distance) computation is a fundamental research topi...
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08/03/2018
Where-and-When to Look: Deep Siamese Attention Networks for Video-based Person Re-identification
Video-based person re-identification (re-id) is a central application in...
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04/30/2018
Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification
Person re-identification (re-ID) requires rapid, flexible yet discrimina...
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08/01/2017
Tensorial Recurrent Neural Networks for Longitudinal Data Analysis
Traditional Recurrent Neural Networks assume vectorized data as inputs. ...
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07/21/2017
What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification
Matching pedestrians across disjoint camera views, known as person re-id...
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07/03/2017
Vectorial Dimension Reduction for Tensors Based on Bayesian Inference
Dimensionality reduction for high-order tensors is a challenging problem...
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06/10/2017
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Person re-identification (re-id) aims to match pedestrians observed by d...
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06/09/2017
Assessing the Performance of Deep Learning Algorithms for Newsvendor Problem
In retailer management, the Newsvendor problem has widely attracted atte...
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05/17/2017
Localized LRR on Grassmann Manifolds: An Extrinsic View
Subspace data representation has recently become a common practice in ma...
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09/21/2016
Partial Least Squares Regression on Riemannian Manifolds and Its Application in Classifications
Partial least squares regression (PLSR) has been a popular technique to ...
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09/21/2016
Matrix Variate RBM Model with Gaussian Distributions
Restricted Boltzmann Machine (RBM) is a particular type of random neural...
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08/30/2016
Low-rank Multi-view Clustering in Third-Order Tensor Space
The plenty information from multiple views data as well as the complemen...
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06/13/2016
Laplacian LRR on Product Grassmann Manifolds for Human Activity Clustering in Multi-Camera Video Surveillance
In multi-camera video surveillance, it is challenging to represent video...
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01/27/2016
Neighborhood Preserved Sparse Representation for Robust Classification on Symmetric Positive Definite Matrices
Due to its promising classification performance, sparse representation b...
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01/21/2016
Partial Sum Minimization of Singular Values Representation on Grassmann Manifolds
As a significant subspace clustering method, low rank representation (LR...
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01/09/2016
Kernelized LRR on Grassmann Manifolds for Subspace Clustering
Low rank representation (LRR) has recently attracted great interest due ...
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01/07/2016
Block-Diagonal Sparse Representation by Learning a Linear Combination Dictionary for Recognition
In a sparse representation based recognition scheme, it is critical to l...
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01/07/2016
Mixture of Bilateral-Projection Two-dimensional Probabilistic Principal Component Analysis
The probabilistic principal component analysis (PPCA) is built upon a gl...
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01/05/2016
Low-Rank Representation over the Manifold of Curves
In machine learning it is common to interpret each data point as a vecto...
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01/05/2016
Matrix Variate RBM and Its Applications
Restricted Boltzmann Machine (RBM) is an importan- t generative model mo...
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01/04/2016
Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds
Sparse subspace clustering (SSC), as one of the most successful subspace...
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01/02/2016
Tensor Sparse and Low-Rank based Submodule Clustering Method for Multi-way Data
A new submodule clustering method via sparse and low-rank representation...
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12/07/2015
Fast Optimization Algorithm on Riemannian Manifolds and Its Application in Low-Rank Representation
The paper addresses the problem of optimizing a class of composite funct...
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09/04/2015
l1-norm Penalized Orthogonal Forward Regression
A l1-norm penalized orthogonal forward regression (l1-POFR) algorithm is...
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04/16/2015
Segmentation of Subspaces in Sequential Data
We propose Ordered Subspace Clustering (OSC) to segment data drawn from ...
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04/08/2015
Low Rank Representation on Grassmann Manifolds: An Extrinsic Perspective
Many computer vision algorithms employ subspace models to represent data...
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04/08/2015
Kernelized Low Rank Representation on Grassmann Manifolds
Low rank representation (LRR) has recently attracted great interest due ...
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04/07/2015