A New Way of Visualizing Curricula Using Competencies: Cosine Similarity and t-SNE

Takamatsu, Kunihiko; Murakami, Katsuhiko; Kozaki, Yasuhiro; Bannaka, Kenya; Noda, Ikuhiro; Raphael-Joel-Wei, Lim; Mitsunari, Kenichiro; Nakamura, Tadashi; Nakata, Yasuo

doi:10.1109/iiai-aai.2018.00084

Cited by 10 publications

(6 citation statements)

References 11 publications

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“…Erdogan et al applied t-SNE on the visualization of human tissue relationships [ 24 ], whilst in another study t-SNE was used as a scalable alternative to create visualizations (projections) enabling insight into the structure of time dependent data sets [ 25 ]. Another example is the report made by Kunihiko et al which suggests visualizing curricula using a combination of cosine similarity, t-SNE, and scatter plots to help students select their courses [ 26 ]. In addition, Chen et al found that the t-SNE algorithm can be used to optimize underwater target radiated noise spectrum features for the purpose of improving the accuracy and efficiency of the classification algorithm [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Clustering of Hyperspectral Paper Data Using t-SNE

2020

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For a suspected forgery that involves the falsification of a document or its contents, the investigator will primarily analyze the document’s paper and ink in order to establish the authenticity of the subject under investigation. As a non-destructive and contactless technique, Hyperspectral Imaging (HSI) is gaining popularity in the field of forensic document analysis. HSI returns more information compared to conventional three channel imaging systems due to the vast number of narrowband images recorded across the electromagnetic spectrum. As a result, HSI can provide better classification results. In this publication, we present results of an approach known as the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm, which we have applied to HSI paper data analysis. Even though t-SNE has been widely accepted as a method for dimensionality reduction and visualization of high dimensional data, its usefulness has not yet been evaluated for the classification of paper data. In this research, we present a hyperspectral dataset of paper samples, and evaluate the clustering quality of the proposed method both visually and quantitatively. The t-SNE algorithm shows exceptional discrimination power when compared to traditional PCA with k-means clustering, in both visual and quantitative evaluations.

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Section: Introductionmentioning

confidence: 99%

Unsupervised Clustering of Hyperspectral Paper Data Using t-SNE

2020

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“…t ‐SNE is a common method for dimension reduction of high‐dimensional data. [ 42 ] Due to its flexibility in dimension reduction and its potential to find structures that other algorithms cannot, t ‐SNE is widely used. The work of feature dimension reduction shows the effectiveness of the MBGRU network, the data points of the SAE network are mixed, and the features extracted by the MBLSTM and MBGRU networks are separated very well, which proves the effectiveness of the proposed method.…”

Section: Methodsmentioning

confidence: 99%

Multi‐block gated recurrent unit network‐based fault diagnosis method for multivariate nonlinear process

et al. 2023

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The increasing complexity of industrial processes brings new challenges to fault diagnosis tasks, and different types of faults have higher and higher requirements on the performance of fault classification models. This paper proposes a novel multivariate nonlinear temporal‐related fault diagnosis method based on gated recurrent units (GRUs). First, to improve the performance of the model in local information extraction and global integration, high dimensional variables are divided into multiple sub‐blocks according to the structure of chemical process units, and a new block normalization method is proposed to improve the performance of local feature extraction. Second, aiming at the slow drifting faults, the GRU network is adopted inside the sub‐block to extract local sparse and nonlinear temporal features. By combining the variance features of variables after block normalization, the performance of the model on multiplicative faults will improve. Finally, aiming at the complex correlation between variables, a new recurrent matrix method is proposed to extract the time transform information inside each variable to improve the comprehensive performance of the model. Through a multi‐level feature integration strategy, the model can be trained in parallel to improve the training speed. The proposed method shows good performance in the Tennessee Eastman process, and the extracted multi‐class features allow the model to be trained end‐to‐end and simultaneously diagnose multiple types of faults.

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“…IR is primarily composed of three fields: enrollment management IR, research IR, and management IR. Actually, we developed some novel KPIs for enrollment management IR and visualization methods to evaluate the quality of education in higher education [3][13][14] [15]. In higher education, evidence-based evaluation or improvement is very important.…”

Section: Key Performance Indicator In Ir and Higher Educationmentioning

confidence: 99%

Combining Key Performance Indicators and Signs of Changes in Students to improve the quality of education in Institutional Research based on Eduinformatics

Takamatsu

Gozu

Kozaki

et al. 2022

IIAI Letters on Institutional Research

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In recent years, most Japanese universities have started to operate departments of institutional research (IR). In IR fields, Key Performance Indicator (KPI) is important to evaluate the quality of education. Eduinformatics, a novel interdisciplinary field proposed by us, provides good analyses that will help us to solve educational problems. This paper emphasizes that we have to consider whether the evaluation of KPIs is the best method to check the quality of education in universities. We discuss the problem of evaluating KPIs to check the quality of higher education and suggest that we have to recognize and understand the limitations of evaluating KPIs. We propose that it is better to evaluate the combination of both KPIs and signs of changes in students to improve the quality of education.

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A New Way of Visualizing Curricula Using Competencies: Cosine Similarity and t-SNE

Cited by 10 publications

References 11 publications

Unsupervised Clustering of Hyperspectral Paper Data Using t-SNE

Unsupervised Clustering of Hyperspectral Paper Data Using t-SNE

Multi‐block gated recurrent unit network‐based fault diagnosis method for multivariate nonlinear process

Combining Key Performance Indicators and Signs of Changes in Students to improve the quality of education in Institutional Research based on Eduinformatics

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