|
|
|
Rui-Yu Li, Yu Guo and Bin Zhang
Nonnegative matrix factorization (NMF) is an efficient method for feature learning in the field of machine learning and data mining. To investigate the nonlinear characteristics of datasets, kernel-method-based NMF (KNMF) and its graph-regularized extens...
ver más
|
|
|
|
|
|
Li-Na Wang, Guoqiang Zhong, Yaxin Shi and Mohamed Cheriet
Most of the dimensionality reduction algorithms assume that data are independent and identically distributed (i.i.d.). In real-world applications, however, sometimes there exist relationships between data. Some relational learning methods have been propo...
ver más
|
|
|
|
|
|
Masahito Kumagai, Kazuhiko Komatsu, Masayuki Sato and Hiroaki Kobayashi
Combinatorial clustering based on the Ising model is drawing attention as a high-quality clustering method. However, conventional Ising-based clustering methods using the Euclidean distance cannot handle irregular data. To overcome this problem, this pap...
ver más
|
|
|
|
|
|
Yang Wang, Jie Liu, Xiaoxiong Zhu, Qingyang Zhang, Shengguo Li and Qinglin Wang
Structured grid-based sparse matrix-vector multiplication and Gauss?Seidel iterations are very important kernel functions in scientific and engineering computations, both of which are memory intensive and bandwidth-limited. GPDSP is a general purpose dig...
ver más
|
|
|
|
|
|
Li Zou, Haowen Cheng and Qianhui Sun
Wind turbine blades are readily damaged by the workplace environment and frequently experience flaws such as surface peeling and cracking. To address the problems of cumbersome operation, high cost, and harsh application conditions with traditional damag...
ver más
|
|
|