Similarity Graph Learning and Non-linear Deep Representations for Spectral Clusterings

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Learning Deep Representations for Graph Clustering

Recently deep learning has been successfully adopted in many applications such as speech recognition and image classification. In this work, we explore the possibility of employing deep learning in graph clustering. We propose a simple method, which first learns a nonlinear embedding of the original graph by stacked autoencoder, and then runs k-means algorithm on the embedding to obtain cluster...

متن کامل

Deep Neural Networks for Learning Graph Representations

In this paper, we propose a novel model for learning graph representations, which generates a low-dimensional vector representation for each vertex by capturing the graph structural information. Different from other previous research efforts, we adopt a random surfing model to capture graph structural information directly, instead of using the samplingbased method for generating linear sequence...

متن کامل

Combining multiple clusterings using similarity graph

Multiple clusterings are produced for various needs and reasons in both distributed and local environments. Combining multiple clusterings into a final clustering which has better overall quality has gained importance recently. It is also expected that the final clustering is novel, robust, and scalable. In order to solve this challenging problem we introduce a new graph-based method. Our metho...

متن کامل

Non-Linear Similarity Learning for Compositionality

Many NLP applications rely on the existence of similarity measures over text data. Although word vector space models provide good similarity measures between words, phrasal and sentential similarities derived from composition of individual words remain as a difficult problem. In this paper, we propose a new method of of non-linear similarity learning for semantic compositionality. In this metho...

متن کامل

Engineering Comparators for Graph Clusterings

A promising approach to compare two graph clusterings is based on using measurements for calculating the distance between them. Existing measures either use the structure of clusterings or quality-based aspects with respect to some index evaluating both clusterings. Each approach suffers from conceptional drawbacks. We introduce a new approach combining both aspects and leading to better result...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Journal of Physics: Conference Series

سال: 2021

ISSN: 1742-6588,1742-6596

DOI: 10.1088/1742-6596/1757/1/012001