The classical development of neural networks has primarily focused on learning mappings
between finite dimensional Euclidean spaces or finite sets. We propose a generalization of …

Kernel methods are known to be effective to analyse complex objects by implicitly
embedding them into some feature space. The approximate class-specific kernel spectral …

With the development of volleyball technology, the frequent competition, the fierce
competition, and the increase of sports load, the requirements for the athletes' own body …

Extracting low-rank and/or sparse structures using matrix factorization techniques has been
extensively studied in the machine learning community. Kernelized matrix factorization …

Logistic regression as a classic classification algorithm has limitations that can only be
applied to linearly separable data. For linearly indivisible data, we use a kernel trick to map it …

Abstract Development of effective and efficient techniques for video analysis is an important
research area in machine learning and computer vision. Matrix factorization (MF) is a …

Matrix sketching is aimed at finding compact representations of a matrix while
simultaneously preserving most of its properties, which is a fundamental building block in …

不同于采样矩阵近似方法, 设计了一种基于随机循环矩阵投影来实现矩阵的近似. 首先,
利用随机采样得到一个初始矩阵的近似轮廓, 然后构造循环嵌入矩阵, 将该循环矩阵作为投影 …

Over the last decades, learning methods using kernels have become very popular. The main
reason is that real data analysis often requires nonlinear methods to detect the …

An essential component in signal processing is to remove various kinds of noise from the
signal. It is possible to introduce noise during the process of signal storage, transmission …