منابع مشابه
Multi-Task Multi-View Clustering for Non-Negative Data
Multi-task clustering and multi-view clustering have severally found wide applications and received much attention in recent years. Nevertheless, there are many clustering problems that involve both multi-task clustering and multi-view clustering, i.e., the tasks are closely related and each task can be analyzed from multiple views. In this paper, for non-negative data (e.g., documents), we int...
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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 ...
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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...
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Multi-task clustering improves the clustering performance of each task by transferring knowledge across related tasks. Most existing multi-task clustering methods are based on the ideal assumption that the tasks are completely related. However, in many real applications, the tasks are usually partially related, and brute-force transfer may cause negative effect which degrades the clustering per...
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Traditionally, spectral clustering is limited to a single objective: finding the normalized min-cut of a single graph. However, many real-world datasets, such as scientific data (fMRI scans of different individuals), social data (different types of connections between people), web data (multi-type data), are generated from multiple heterogeneous sources. How to optimally combine knowledge from ...
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ژورنال
عنوان ژورنال: IEEE Transactions on Knowledge and Data Engineering
سال: 2016
ISSN: 1041-4347
DOI: 10.1109/tkde.2016.2603983