Incremental kernel spectral clustering for online learning of non-stationary data

TL;DR: The theoretical analysis and experimental results show that the novel incremental multi-view spectral clustering method is significantly faster in efficiency than the existing state-of-the-art non-incremental ones and is comparable or even better in clustering quality.

TL;DR: The results showed that this method could effectively mine the deep information of intelligent equipment and provide more valuable information about design-assisting designers in designing better intelligent equipment.

TL;DR: This chapter reviews the main literature related to kernel spectral clustering (KSC), an approach to clustering cast within a kernel-based optimization setting, and shows how it is possible to handle large-scale data.

TL;DR: This paper develops a mathematical localization model by analyzing the location report created by the HST, and applies two sparse optimization algorithms to improve the sparsity of both least squares support vector machine (LSSVM) and weighted LSSVM models.

TL;DR: A simple spectral clustering algorithm that can be implemented using a few lines of Matlab is presented, and tools from matrix perturbation theory are used to analyze the algorithm, and give conditions under which it can be expected to do well.

Abstract: The proceedings of the 2001 Neural Information Processing Systems (NIPS) Conference. The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented at the 2001 conference. Bradford Books imprint

TL;DR: Here the authors haven’t even started the project yet, and already they’re forced to answer many questions: what will this thing be named, what directory will it be in, what type of module is it, how should it be compiled, and so on.

TL;DR: Eigenvalues and the Laplacian of a graph Isoperimetric problems Diameters and eigenvalues Paths, flows, and routing Eigen values and quasi-randomness