Auto-Weighted Incomplete Multi-View Clustering
نویسندگان
چکیده
منابع مشابه
Weighted Multi-view Clustering with Feature Selection
In recent years, combining multiple sources or views of datasets for data clustering has been a popular practice for improving clustering accuracy. As different views are different representations of the same set of instances, we can simultaneously use information from multiple views to improve the clustering results generated by the limited information from a single view. Previous studies main...
متن کاملRobust auto-weighted multi-view subspace clustering with common subspace representation matrix
In many computer vision and machine learning applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is a powerful technology to find the underlying subspaces and cluster data points correctly. However, traditional subspace clustering methods can only be applied on data from one source, and how to extend these methods and enable the extensions to combine...
متن کاملPartial Multi-View Clustering
Real data are often with multiple modalities or coming from multiple channels, while multi-view clustering provides a natural formulation for generating clusters from such data. Previous studies assumed that each example appears in all views, or at least there is one view containing all examples. In real tasks, however, it is often the case that every view suffers from the missing of some data ...
متن کاملMulti-view Recognition Using Weighted View Selection
In this paper, we present an algorithm for multi-view recognition in a distributed camera setting that learns which viewpoints are most discriminative for particular instances of ambiguity. Our method is built on top of 2D recognition algorithms and casts view selection as the problem of optimizing kernel weights in multiple kernel learning. The main contribution is a locality-sensitive meta-tr...
متن کاملFrom Ensemble Clustering to Multi-View Clustering
Multi-View Clustering (MVC) aims to find the cluster structure shared by multiple views of a particular dataset. Existing MVC methods mainly integrate the raw data from different views, while ignoring the high-level information. Thus, their performance may degrade due to the conflict between heterogeneous features and the noises existing in each individual view. To overcome this problem, we pro...
متن کاملذخیره 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: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3012500