Adaptive‐Weighted Multiview Deep Basis Matrix Factorization for Multimedia Data Analysis

Li, Shicheng; Liu, Qinghua; Dai, Jiangyan; Wang, Wenle; Gui, Xiaolin; Yi, Yugen

doi:10.1155/2021/5526479

Cited by 4 publications

(2 citation statements)

References 47 publications

(56 reference statements)

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“…Different techniques to multi-view clustering have been explored before, but they have not been analyzed in terms of dimensionality reduction in the representation of multi labelled data in supervised learning. In order to identify the text, the supervised learning technique defines the labelled information based on its features [20,21]. It is thus crucial to find data sources for unsupervised learning with multiple labelling [22,23].…”

Section: Introductionmentioning

confidence: 99%

A Meta Heuristic Multi-View Data Analysis over Unconditional Labeled Material: An Intelligence OCMHAMCV

Kolli

Krishna

Sreedevi³

2022

SCPE

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Artificial intelligence has been provided powerful research attributes like data mining and clustering for reducing bigdata functioning. Clustering in multi-labeled categorical analysis gives huge amount of relevant data that explains evaluation and portrayal of qualities as trending notion. A wide range of scenarios, data from many dimensions may be used to provide efficient clustering results. Multi-view clustering techniques had been outdated, however they all provide less accurate results when a single clustering of input data is applied. Numerous data groups are conceivable due to diversity of multi-dimensional data, each with its own unique set of viewpoints. When dealing multi-view labelled data, obtaining quantifiable and realistic cluster results may be challenge. This study provides unique strategy termed OCMHAMCV (Orthogonal Constrained Meta Heuristic Adaptive Multi-View Cluster). In beginning, OMF approach used to cluster similar labelled sample data into prototypes of dimensional clusters of low-dimensional data. Utilize adaptive heuristics integrate complementary data several dimensions complexity of computational analysis data representation data in appropriate orthonormality constrained viewpoint. Studies on massive data sets reveal that proposed method outperforms more traditional multi-view clustering techniques scalability and efficiency. The performance measures like accuracy 98.32%, sensitivity 93.42%, F1-score 98.53% and index score 96.02% has been attained, which was good improvement. Therefore it is proved that proposed methodology suitable for document summarization application for future scientific analysis.

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Section: Introductionmentioning

confidence: 99%

A Meta Heuristic Multi-View Data Analysis over Unconditional Labeled Material: An Intelligence OCMHAMCV

Kolli

Krishna

Sreedevi³

2022

SCPE

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“…All these approaches are not utilized with elements in matrix in the process of decomposition of matrix; it means maximization of matrix representation consist negative elements in representation of data in low-dimensions. As of late, deep learning has shown extraordinary execution in include portrayal undertakings [18][19][20]. Consequently, numerous analysts have brought profound learning into lattice factorization and proposed countless profound component portrayal strategies [21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Orthogonal Constrained Meta Heuristic Adaptive Multi-View Clustering Over Multi Labeled Categorical Data Analysis

Kolli

Sreed

2021

Preprint

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In data mining, clustering is the one of the efficient research concept in real time data analysis, evaluation of attribute representation in clustering is main issue in artificial intelligence related research areas. Multi labeled clustering gives high amount of valuable data, which describes the evaluation and representation of attribute be the trending concept in multi labeled categorical data analysis. Multi dimensional clustering is combined complementary data from different dimensions to provide efficient clustering results in various conditions. Different multi view clustering techniques are proposed traditionally but they can give output as single clustering with input data. Because of multiplicity, multi dimensional data can have different grouping data which are reasonable consist perspective attributes. So how to find measurable and reasonable cluster results which are represented in multi view labeled data is still challenging task, so that in this paper, we propose a novel approach i.e. Orthogonal Constrained Meta Heuristic Adaptive Multi-View Clustering (OCMHAMVC) to represent data as a cluster with different categories. Based on multi labeled data, first proposed approach evaluates low dimensional data using optimized matrix factorization (OMF) method and clusters the similar labeled sample data into prototype cluster of dimensional data. After that we represent data in desirable orthonormality constrained view of data using adaptive heuristic to combine complementary data from different dimensions, also provide complexity in computational analysis of data representation. Experimental results of proposed approach applied on high amount of multi view data gives scalable and efficient performance with comparison to traditional multi view related clustering approaches.

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Unsupervised Anomaly Detection for Glaucoma Diagnosis

Zhou

Yuan²,

et al. 2021

Wireless Communications and Mobile Computing

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With the rapid development of high tech, Internet of Things (IoT) and artificial intelligence (AI) achieve a series of achievements in the healthcare industry. Among them, automatic glaucoma diagnosis is one of them. Glaucoma is second leading cause of blindness in the world. Although many automatic glaucoma diagnosis approaches have been proposed, they still face the following two challenges. First, the data acquisition of diseased images is extremely expensive, especially for disease with low occurrence, leading to the class imbalance. Second, large-scale labeled data are hard to obtain in medical image domain. The aforementioned challenges limit the practical application of these approaches in glaucoma diagnosis. To address these disadvantages, this paper proposes an unsupervised anomaly detection framework based on sparse principal component analysis (SPCA) for glaucoma diagnosis. In the proposed approach, we just employ the one-class normal (nonglaucoma) images for training, so the class imbalance problem can be avoided. Then, to distinguish the glaucoma (abnormal) images from the normal images, a feature set consisting of segmentation-based features and image-based features is extracted, which can capture the shape and textural changes. Next, SPCA is adopted to select the effective features from the feature set. Finally, with the usage of the extracted effective features, glaucoma diagnosis can be automatically accomplished via introducing the T 2 statistic and the control limit, overcoming the issue of insufficient labeled samples. Extensive experiments are carried out on the two public databases, and the experimental results verify the effectiveness of the proposed approach.

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Adaptive‐Weighted Multiview Deep Basis Matrix Factorization for Multimedia Data Analysis

Cited by 4 publications

References 47 publications

A Meta Heuristic Multi-View Data Analysis over Unconditional Labeled Material: An Intelligence OCMHAMCV

A Meta Heuristic Multi-View Data Analysis over Unconditional Labeled Material: An Intelligence OCMHAMCV

Orthogonal Constrained Meta Heuristic Adaptive Multi-View Clustering Over Multi Labeled Categorical Data Analysis

Unsupervised Anomaly Detection for Glaucoma Diagnosis

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