Hyperspectral image (HSI) clustering is a challenging task due to the high complexity of HSI data. Subspace has been proven be powerful for exploiting intrinsic relationship between data points. Despite impressive performance in clustering, traditional subspace methods often ignore inherent structural information among In this article, we revisit with graph convolution and present novel framewo...

This paper presents a new subspace modeling and selection approach for noisy speech recognition. In subspace modeling, we develop factor analysis (FA) for representing noisy speech. FA is a data generation model where the common factors are extracted with factor loading matrix and specific factors. We bridge the connection of FA to signal subspace (SS) approach. Interestingly, FA partitions noi...

Abstract We study the problem of determining minimum number $f(n,k,d)$ affine subspaces codimension $d$ that are required to cover all points $\mathbb{F}_2^n\setminus \{\vec{0}\}$ at least $k$ times while covering origin most $k - 1$ times. The case $k=1$ is a classic result Jamison, which was independently obtained by Brouwer and Schrijver for $d = . value $f(n,1,1)$ also follows from well-kno...

A new guideline for proper allocation of multipoles in the multiple multipole method (MMP) is proposed. In an ‘a posteriori’ approach, subspace fitting (SSF) is used to find the best location of multipole expansions for the two dimensional dielectric scattering problem. It is shown that the best location of multipole expansions (regarding their global approximating power) coincides with the med...

We present algorithms for reducing large circuits, described at SPICE-level detail, to much smaller ones with similar input–output behavior. A key feature of our method, called timevarying Padé (TVP), is that it is capable of reducing time-varying linear systems. This enables it to capture frequency-translation and sampling behavior, important in communication subsystems such as mixers and swit...