Multi-View Clustering of Microbiome Samples by Robust Similarity Network Fusion and Spectral Clustering
نویسندگان
چکیده
منابع مشابه
Neighborhood Co-regularized Multi-view Spectral Clustering of Microbiome Data
In many unsupervised learning problems data can be available in different representations, often referred to as views. By leveraging information from multiple views we can obtain clustering that is more robust and accurate compared to the one obtained via the individual views. We propose a novel algorithm that is based on neighborhood co-regularization of the clustering hypotheses and that sear...
متن کاملRobust and efficient multi-way spectral clustering
We present a new algorithm for spectral clustering based on a column-pivoted QR factorization that may be directly used for cluster assignment or to provide an initial guess for k-means. Our algorithm is simple to implement, direct, and requires no initial guess. Furthermore, it scales linearly in the number of nodes of the graph and a randomized variant provides significant computational gains...
متن کاملCo-regularized Multi-view Spectral Clustering
In many clustering problems, we have access to multiple views of the data each of which could be individually used for clustering. Exploiting information from multiple views, one can hope to find a clustering that is more accurate than the ones obtained using the individual views. Often these different views admit same underlying clustering of the data, so we can approach this problem by lookin...
متن کاملRobust Localized Multi-view Subspace Clustering
In multi-view clustering, different views may have different confidence levels when learning a consensus representation. Existing methods usually address this by assigning distinctive weights to different views. However, due to noisy nature of realworld applications, the confidence levels of samples in the same viewmay also vary. Thus considering a unified weight for a view may lead to suboptim...
متن کاملMulti-objective Multi-view Spectral Clustering via Pareto Optimization
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 ...
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/ACM Transactions on Computational Biology and Bioinformatics
سال: 2017
ISSN: 1545-5963,1557-9964,2374-0043
DOI: 10.1109/tcbb.2015.2474387