*
instances will be regarded as the same class
and be connected with the same weight,
which in turn pulls these missing instances
together in the low-dimensional subspace
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T
H
A
N
K
Y
O
U
The proposed method significantly outperforms the other methods on the above multi-view databases with all kinds of incomplete cases. For instance, on the handwritten digit database (Table II), the proposed method achieves 3% and 6% improvement of ACC and NMI in comparison with the second best method. 【Clarifying experimental results and analyze the results by giving an example. 】
Example two
*
Complexity
Academic writing is gramatically more complex than other forms of writing.
Inspired by this motivation, Gao et al. [30]
*
proposed the multi-view subspace clustering
Objectivity
Examples
From Tables II–V and Fig. 3, we can find that PMVC, IMG, DCNMF, GPMVC, and the proposed method can achieve much better performance than BSV and Concat in most cases. 【Achieve objective effects √ , focus on what you can demonstrate rather than what you can believe. And also use the strategy of hedging, make the writing safer and more objective.】
Accuracy
Provide factual information, figures or tables to achieve more precision.
标题文本
lorem ipsum dolor sit amet, consectetur adipisicing elit, sed ea commodoconsequat.
ห้องสมุดไป่ตู้
Explicitness
Explicit in expressing ideas
• In this paper, we propose a general framework for incomplete multi-view clustering. The proposed method is the first work that exploits the graph learning and spectral clustering techniques to learn the common representation for incomplete multi-view clustering.
The main characteristics of academic writing style including
Formality Complexity Explicitness Accuracy Conciseness Objectivity Responsibility
Formality
Explicit in signposting the organization of the ideas
• First, owing to the good performance of low-rank representation in discovering the intrinsic subspace structure of data, we adopt it to adaptively construct the graph of each view. Second, a spectral constraint is used to achieve the low-dimensional representation of each view based on the spectral clustering. Third, we further introduce a co-regularization term to learn the common representation of samples for all views, and then use the k-means to partition the data into their respective groups. An efficient iterative algorithm is provided to optimize the model.
whether they are from the same cluster or
not.
Responsible
● This work was supported in part by the National Basic Research Program of China (Grant 2012CB316304), the National Natural Science Foundation of China (Grants 61272331, 91338202 and 91438105), the Strategic Priority Research Program of the Chinese Academy of Sciences Grant (XDA06030200), Beijing Key Lab of Intelligent Telecommunication Software and Multimedia (ITSM201502), Guangxi Key Laboratory of Trusted Software (KX201418). 【Acknowledge the funding agency giving a grant】
(MVSC), which focuses on learning a
consensus cluster indicator matrix for
clustering.
【Grammatically: use clauses and embedding】
This is mainly because that these missing
Conciseness
Example one
The proposed method is robust to noise to some extent. Especially, by introducing the sparse error term to compensate the noise, the proposed method can greatly reduce the negative influence of noise, which is beneficial to discover the intrinsic structure of the noisy data. 【Concisely explain the advantages and significance of the proposed new method.】
The Style of Academic Writing
.
Academic texts should conform to a unique set of stylistic rules, which vary only slightly across different disciplines. Understanding the rules of academic style and the corresponding writing skills will enable us to produce more effective academic papers.