Theoretical developments for interpreting kernel spectral clustering from alternative viewpoints

Abstract

To perform an exploration process over complex structured data within unsupervised settings, the so-called kernel spectral clustering (KSC) is one of the most recommended and appealing approaches, given its versatility and elegant formulation. In this work, we explore the relationship between (KSC) and other well-known approaches, namely normalized cut clustering and kernel k-means. To do so, we first deduce a generic KSC model from a primal-dual formulation based on least-squares support-vector machines (LS-SVM). For experiments, KSC as well as other consider methods are assessed on image segmentation tasks to prove their usability.

Semi Supervised NCC: Introducing Prior Knowledge To Normalized Cut Clustering

“…The latter deals with problems where the output label vector is unavailable, thus the aim is to unveil useful information about the underlying structure of the data. This encompasses frameworks such as Dimensionality Reduction (DR) [2], [3], Data Visualization (DV) [4], [5], and Clustering [6], among others. For instance, the aim of clustering techniques is to partition the data into disjoint groups such that objects in the same cluster are similar in some ways.…”

Semi Supervised NCC: Introducing Prior Knowledge To Normalized Cut Clustering

“…The latter deals with problems where the output label vector is unavailable, thus the aim is to unveil useful information about the underlying structure of the data. This encompasses frameworks such as Dimensionality Reduction (DR) [2], [3], Data Visualization (DV) [4], [5], and Clustering [6], among others. For instance, the aim of clustering techniques is to partition the data into disjoint groups such that objects in the same cluster are similar in some ways.…”