Membership Embedding Space Approach and Spectral Clustering
نویسندگان
چکیده
The data representation strategy termed “Membership Embedding” is a type of similarity-based representation that uses a set of data items in an input space as reference points (probes), and represents all data in terms of their membership to the fuzzy concepts represented by the probes. The technique has been proposed as a concise representation for improving the data clustering task. In this contribution, it is shown that this representation strategy yields a spectral clustering formulation, and this may account for the improvement in clustering performance previously reported. Then the problem of selecting an appropriate set of probes is discussed in view of this result.
منابع مشابه
Semi-Supervised Learning Using Kernel Spectral Clustering Core Model
A multi-class semi-supervised learning algorithm formulated as a regularized kernel spectral clustering (KSC) approach is proposed. The method is bale to address both semi-supervised classification and clustering. In addition a low embedding dimension is utilized to reveal the existing number of clusters. Thanks to the Nyström approximation technique, the approach can be scaled up for analyzing...
متن کاملClustering in the membership embedding space
In several applications of data mining to high-dimensional data, clustering techniques developed for low-to-moderate sized problems obtain unsatisfactory results. This is an aspect of the curse of dimensionality issue. A traditional approach is based on representing the data in a suitable similarity space instead of the original high-dimensional attribute space. In this paper, we propose a solu...
متن کاملConstrained Spectral Clustering with Distance Metric Learning
Spectral clustering is a flexible clustering technique that finds data clusters in the spectral embedding space of the data. It doesn’t assume convexity of the shape of clusters, and is able to find non-linear cluster boundaries. Constrained spectral clustering aims at incorporating user-defined pairwise constraints in to spectral clustering. Typically, there are two kinds of pairwise constrain...
متن کاملUnsupervised Classification of X-Ray Mapping Images of Polished Sections
X-ray mapping images of polished sections are classified using two unsupervised clustering algorithms. The methods applied are the k-means algorithm and an extended spectral fuzzy c-means algorithm. The extentions include new types of memberships that are related to the contextual information. In addition to the traditional spectral membership we apply a spatial membership and a parental member...
متن کاملA Probabilistic Approach for Optimizing Spectral Clustering
Spectral clustering enjoys its success in both data clustering and semisupervised learning. But, most spectral clustering algorithms cannot handle multi-class clustering problems directly. Additional strategies are needed to extend spectral clustering algorithms to multi-class clustering problems. Furthermore, most spectral clustering algorithms employ hard cluster membership, which is likely t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2007