scholarly journals From Ensemble Clustering to Multi-View Clustering

2017 ◽  
Author(s):  
Zhiqiang Tao ◽  
Hongfu Liu ◽  
Sheng Li ◽  
Zhengming Ding ◽  
Yun Fu
Keyword(s):  
Cluster Structure ◽  
Low Rank ◽  
Ensemble Clustering ◽  
Optimization Framework ◽  
Heterogeneous Features ◽  
Level Information ◽  
Partition Space ◽  
Real World Datasets ◽  
Consensus Partition ◽  
High Level

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 propose a novel Multi-View Ensemble Clustering (MVEC) framework to solve MVC in an Ensemble Clustering (EC) way, which generates Basic Partitions (BPs) for each view individually and seeks for a consensus partition among all the BPs. By this means, we naturally leverage the complementary information of multi-view data in the same partition space. Instead of directly fusing BPs, we employ the low-rank and sparse decomposition to explicitly consider the connection between different views and detect the noises in each view. Moreover, the spectral ensemble clustering task is also involved by our framework with a carefully designed constraint, making MVEC a unified optimization framework to achieve the final consensus partition. Experimental results on six real-world datasets show the efficacy of our approach compared with both MVC and EC methods.

scholarly journals Inference of Mood State Indices by Using a Multimodal High-Level Information Fusion Technique

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chih-Hua Tai ◽  
Kuo-Hsuan Chung ◽  
Ya-Wen Teng ◽  
Feng-Ming Shu ◽  
Yue-Shan Chang
Keyword(s):  
Information Fusion ◽  
Mood State ◽  
Fusion Technique ◽  
Level Information ◽  
Information Fusion Technique ◽  
High Level

scholarly journals A non-convex optimization framework for large-scale low-rank matrix factorization

2020 ◽  
Author(s):  
Sajad Fathi Hafshejani ◽  
Saeed Vahidian ◽  
Zahra Moaberfard ◽  
Reza Alikhani ◽  
Bill Lin
Keyword(s):  
Matrix Factorization ◽  
Large Scale ◽  
Significant Loss ◽  
Low Rank ◽  
Function Evaluation ◽  
Dimensional Manifold ◽  
Step Size ◽  
Rank Matrix ◽  
Real World Datasets ◽  
Low Rank Matrix

Low-rank matrix factorization problems such as non negative matrix factorization (NMF) can be categorized as a clustering or dimension reduction technique. The latter denotes techniques designed to find representations of some high dimensional dataset in a lower dimensional manifold without a significant loss of information. If such a representation exists, the features ought to contain the most relevant features of the dataset. Many linear dimensionality reduction techniques can be formulated as a matrix factorization. In this paper, we combine the conjugate gradient (CG) method with the Barzilai and Borwein (BB) gradient method, and propose a BB scaling CG method for NMF problems. The new method does not require to compute and store matrices associated with Hessian of the objective functions. Moreover, adopting a suitable BB step size along with a proper nonmonotone strategy which comes by the size convex parameter $\eta_k$, results in a new algorithm that can significantly improve the CPU time, efficiency, the number of function evaluation. Convergence result is established and numerical comparisons of methods on both synthetic and real-world datasets show that the proposed method is efficient in comparison with existing methods and demonstrate the superiority of our algorithms.

High-level information fusion and situation awareness

2009 ◽  
Vol 10 (1) ◽  
pp. 2-5 ◽  
Author(s):  
Mieczyslaw M. Kokar ◽  
Gee Wah Ng
Keyword(s):  
Information Fusion ◽  
Situation Awareness ◽  
Level Information ◽  
High Level

Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count Prediction

2022 ◽  
Vol 40 (1) ◽  
pp. 1-29
Author(s):  
Siqing Li ◽  
Yaliang Li ◽  
Wayne Xin Zhao ◽  
Bolin Ding ◽  
Ji-Rong Wen
Keyword(s):  
Peer Review ◽  
Prediction Models ◽  
Citation Count ◽  
Specific Aspect ◽  
Semantic Features ◽  
Predictive Capacity ◽  
Topic Distribution ◽  
Real World Datasets ◽  
Data Signal ◽  
High Level

Citation count prediction is an important task for estimating the future impact of research papers. Most of the existing works utilize the information extracted from the paper itself. In this article, we focus on how to utilize another kind of useful data signal (i.e., peer review text) to improve both the performance and interpretability of the prediction models. Specially, we propose a novel aspect-aware capsule network for citation count prediction based on review text. It contains two major capsule layers, namely the feature capsule layer and the aspect capsule layer, with two different routing approaches, respectively. Feature capsules encode the local semantics from review sentences as the input of aspect capsule layer, whereas aspect capsules aim to capture high-level semantic features that will be served as final representations for prediction. Besides the predictive capacity, we also enhance the model interpretability with two strategies. First, we use the topic distribution of the review text to guide the learning of aspect capsules so that each aspect capsule can represent a specific aspect in the review. Then, we use the learned aspect capsules to generate readable text for explaining the predicted citation count. Extensive experiments on two real-world datasets have demonstrated the effectiveness of the proposed model in both performance and interpretability.

Governing Information Security

2013 ◽  
pp. 29-45
Author(s):  
Yu “Andy” Wu ◽  
Carol Stoak Saunders
Keyword(s):  
Information Technology ◽  
Information Security ◽  
Decision Rights ◽  
Security Governance ◽  
Level Information ◽  
Information Security Governance ◽  
Allocation Patterns ◽  
High Level ◽  
Decisional Authority ◽  
Selection Of

Governance of the information security function is critical to effective security. In this paper, the authors present a conceptual model for security governance from the perspective of decision rights allocation. Based on Da Veiga and Eloff’s (2007) framework for security governance and two high-level information security documents published by the National Institute of Standards and Technology (NIST), the authors present seven domains of information security governance. For each of the governance domains, they propose a main decision type, using the taxonomy of information technology decisions defined by Weill and Ross (2004). This framework recommends the selection of decision rights allocation patterns that are proper to those decision types to ensure good security decisions. As a result, a balance can be achieved between decisional authority and responsibility for information security.

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