Multi-view data clustering via non-negative matrix factorization with manifold regularization

In this study, the authors intend to analyze factors contributing to research performance using Backpropagation Neural Network and Support Vector Machine. The analyzing factors contributing to lecturer research performance start from defining the features. The next stage is to collect datasets based on defining features. Then transform the raw dataset into data ready to be processed. After the data is transformed, the next stage is the selection of features. Before the selection of features, the target feature is determined, namely research performance. The selection of features consists of Chi-Square selection (U), and Pearson correlation coefficient (CM). The selection of features produces eight factors contributing to lecturer research performance are Scientific

Section: Data Mining Modelingmentioning

confidence: 99%

Analyzing Factors Contributing to Research Performance using Backpropagation Neural Network and Support Vector Machine

2022

“…Three way clustering scheme is used in [24] to find out the relationship between data items and clusters.A multi view clustering technique by customizing the K-means algorithm is also suggesting in this paper. [25] also describing a multi view data clustering scheme with the help of non negative matrix factorization and a solution is proposed from diverse views by preserving the geometrical structure of the data.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Machine Learning-based Text Summarization in the Malayalam Language

2022

Automatic text summarization is a procedure that packs enormous content into a more limited book that incorporates significant data. Malayalam is one of the toughest languages utilized in certain areas of India, most normally in Kerala and in Lakshadweep. Natural language processing in the Malayalam language is relatively low due to the complexity of the language as well as the scarcity of available resources. In this paper, a way is proposed to deal with the text summarization process in Malayalam documents by training a model based on the Support Vector Machine classification algorithm. Different features of the text are taken into account for training the machine so that the system can output the most important data from the input text. The classifier can classify the most important, important, average, and least significant sentences into separate classes and based on this, the machine will be able to create a summary of the input document. The user can select a compression ratio so that the system will output that much fraction of the summary. The model performance is measured by using different genres of Malayalam documents as well as documents from the same domain. The model is evaluated by considering content evaluation measures precision, recall, F score, and relative utility. Obtained precision and recall value shows that the model is trustable and found to be more relevant compared to the other summarizers.

“…This requires data clustering and outlier analysis to ensure the quality of landmark matching. Clustering algorithms have been extensively studied in recent years [34,35,36,37]. Local Outlier Factor (LOF) [38] can be used to find outliers and remove these matching pairs.…”

Section: 𝐶𝐶𝐶𝐶𝐶𝐶(𝐿𝐿𝐿𝐿_𝐹𝐹 𝐴𝐴mentioning

confidence: 99%

Efficient Visual Place Recognition by Adaptive CNN Landmark Matching

2021

Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.