We study the convergence and the rate of convergence of a local manifold learning algorithm: LTSA [13]. The main technical tool is the perturbation analysis on the linear invarian...
eriodically time-varying (LPTV) abstractions are useful for a variety of communication and computer subsystems. In this paper, we present a novel operator-based model-order reduct...
Linear dimensionality reduction (LDR) is quite important in pattern recognition due to its efficiency and low computational complexity. In this paper, we extend the two-class Chern...
This paper presents an efficient method to reduce complexities of a linear network in s-domain. The new method works on circuit matrices directly and reduces the circuit complexi...
We study the problem of efficiently removing equal frequency n-gram substrings from an n-gram set, formally called Statistical Substring Reduction (SSR). SSR is a useful operatio...