Search Results - Kernel tensor sparse coding

Kernel Tensor Sparse Coding Model for Precise Crop Classification of UAV Hyperspectral Image by Yang, Lixia, Zhang, Rui, Bao, Yajun, Yang, Shuyuan, Jiao, Licheng

“…In this letter, a kernel tensor sparse coding model (KTSCM) is proposed for precise crop classification of unmanned aerial vehicle (UAV…”
Get full text

Precise crop classification of UAV hyperspectral imagery using kernel tensor slice sparse coding based classifier by Yang, Lixia, Chen, Jinwei, Zhang, Rui, Yang, Shuyuan, Zhang, Xinyu, Jiao, Licheng

“…•The KTSSOMP algorithm adaptively learns the sparse coding coefficients of KTSSCC.•There are very few parameters in our proposed KTSSCC model…”
Get full text

Two Layer Tensor Form Convolutional Sparse Coding for Stereo Matching by Cheng, Chunbo, Cui, Wenjing

“… Therefore, it is very important to establish a deep tensor model. In this paper, we propose a two layer tensor form convolutional sparse coding model, which can automatically learn the deep convolutional kernel…”
Get full text

Deep High-order Tensor Convolutional Sparse Coding for Stereo Matching by Cui, Wenjing, Cheng, Chunbo

“… Therefore, it is very important to establish a deep high order tensor model. In this paper, we propose a deep high-order tensor convolutional sparse coding model, which can automatically learn the deep convolutional kernel…”
Get full text

Deep High-Order Tensor Convolutional Sparse Coding for Hyperspectral Image Classification by Cheng, Chunbo, Li, Hong, Peng, Jiangtao, Cui, Wenjing, Zhang, Liming

“… In this article, a deep high-order tensor convolutional sparse coding (CSC) model is proposed, which can be used to train deep high-order filters…”
Get full text

Large-Scale Subspace Clustering Based on Purity Kernel Tensor Learning by Zheng, Yilu, Zhao, Shuai, Zhang, Xiaoqian, Xu, Yinlong, Peng, Lifan

“…) tasks challenging to execute effectively. To address these issues, we propose a large-scale subspace clustering method based on pure kernel tensor learning (PKTLS2C…”
Get full text

Onyx: A 12nm 756 GOPS/W Coarse-Grained Reconfigurable Array for Accelerating Dense and Sparse Applications by Koul, Kalhan, Strange, Maxwell, Melchert, Jackson, Carsello, Alex, Mei, Yuchen, Hsu, Olivia, Kong, Taeyoung, Chen, Po-Han, Ke, Huifeng, Zhang, Keyi, Liu, Qiaoyi, Nyengele, Gedeon, Balasingam, Akhilesh, Adivarahan, Jayashree, Sharma, Ritvik, Xie, Zhouhua, Torng, Christopher, Emer, Joel, Kjolstad, Fredrik, Horowitz, Mark, Raina, Priyanka

“…Onyx is the first fully programmable accelerator for arbitrary sparse tensor algebra kernels…”
Get full text

Unified Compilation for Lossless Compression and Sparse Computing by Donenfeld, Daniel, Chou, Stephen, Amarasinghe, Saman

“…This paper shows how to extend sparse tensor algebra compilers to support lossless compression techniques, including variants of run-length encoding and Lempel-Ziv compression…”
Get full text

Low-Rank Tensor Patching Based on Convolutional Sparse Coding for Communication Data Repair by Hu, Yi, Yao, Yuyi, Cheng, Zhen

“…). Based on the low-rank nature of the tensor, adding convolutional sparse coding (CSC) can well represent the characteristics of the high-frequency part of the information to handle the details while recovering the global information…”
Get full text

Multimodal classification of drug-naïve first-episode schizophrenia combining anatomical, diffusion and resting state functional resonance imaging by Zhuang, Huixiang, Liu, Ruihao, Wu, Chaowei, Meng, Ziyu, Wang, Danni, Liu, Dengtang, Liu, Manhua, Li, Yao

“…•To reduce the feature dimension of multimodal data, sparse coding is applied for feature selection and multi-kernel support vector machine is applied for feature combination and classification…”
Get full text

Direct Conversion: Accelerating Convolutional Neural Networks Utilizing Sparse Input Activation by Lee, Won-Hyuk, Roh, Si-Dong, Park, Sangki, Chung, Ki-Seok

“… If weights are sparse, most results of convolution operations will be zeroes. Although several studies have proposed methods to utilize the weight sparsity to avoid…”
Get full text

Hyperspectral Imaging and Applications

“…Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by…”
Get full text

Spectral-Spatial KerSparseBands Selector by Wang, Min, Feng, Zhixi, Yang, Shuyuan

“… to the spectral-spatial joint sparse coding (SC) of labels. Moreover, a new tensor-multiple measurement vector (TMMV…”
Get full text

Sparse Kronecker Canonical Polyadic Decomposition for Convolutional Neural Networks Compression by Qi, Mengmeng, Wang, Dingheng, Yang, Wei, Wang, Fuyong, Liu, Zhongxin, Chen, Zengqiang

“… Among the numerous approaches to lightweighting, tensor decomposition methods have demonstrated their unique advantages such as conciseness, flexibility, and the theory of low-rank approximation…”
Get full text

Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach by Harandi, Mehrtash T, Sanderson, Conrad, Hartley, Richard, Lovell, Brian C

“…), we propose to perform sparse coding by embedding Riemannian manifolds into reproducing kernel Hilbert spaces…”
Get full text

Sparse machine learning models in bioinformatics by Li, Yifeng

“…The meaning of parsimony is twofold in machine learning: either the structure or (and) the parameter of a model can be sparse…”
Get full text

Random projections on manifolds of Symmetric Positive Definite matrices for image classification by Alavi, Azadeh, Wiliem, Arnold, Kun Zhao, Lovell, Brian C., Sanderson, Conrad

“…) embedding into Reproducing Kernel Hilbert Spaces (RKHS). While embedding into tangent spaces allows the use of existing Euclidean-based learning algorithms, manifold shape is only approximated which can cause loss of discriminatory information…”
Get full text

Random Projections on Manifolds of Symmetric Positive Definite Matrices for Image Classification by Alavi, Azadeh, Arnold Wiliem, Zhao, Kun, Lovell, Brian C, Sanderson, Conrad

“…) embedding into Reproducing Kernel Hilbert Spaces (RKHS). While embedding into tangent spaces allows the use of existing Euclidean-based learning algorithms, manifold shape is only approximated which can cause loss of discriminatory information…”
Get full text

Face recognition and facial attribute analysis from unconstrained visual data by Ho, Huy Tho

“…Analyzing human faces from visual data has been one of the most active research areas in the computer vision community. However, it is a very challenging…”
Get full text