Publications
Proceedings, refereed
Multimedia datasets: challenges and future possibilities
In International conference on multimedia modeling, 2023.Status: Published
Multimedia datasets: challenges and future possibilities
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2023 |
| Conference Name | International conference on multimedia modeling |
| DOI | 10.1007/978-3-031-27818-1_58 |
Principal Components Analysis Based Imputation for Logistic Regression
In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. {Springer, 2023.Status: Published
Principal Components Analysis Based Imputation for Logistic Regression
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2023 |
| Conference Name | International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems |
| Pagination | 28–36 |
| Publisher | {Springer |
ScopeSense: An 8.5-Month Sport, Nutrition, and Lifestyle Lifelogging Dataset
In International Conference on Multimedia Modeling (MMM). Vol. 13833. Cham: Springer International Publishing, 2023.Status: Published
ScopeSense: An 8.5-Month Sport, Nutrition, and Lifestyle Lifelogging Dataset
Nowadays, most people have a smartphone that can track their everyday activities. Furthermore, a significant number of people wear advanced smartwatches to track several vital biomarkers in addition to activity data. However, it is still unclear how these data can actually be used to improve certain aspects of people’s lives. One of the key challenges is that the collected data is often massive and unstructured. Therefore, a link to other important information (e.g., when, what, and how much food was consumed) is required. It is widely believed that such detailed and structured longitudinal data about a person is essential to model and provide personalized and precise guidance. Despite the strong belief of researchers about the power of such a data-driven approach, respective datasets have been difficult to collect. In this study, we present a unique dataset from two individuals performing a structured data collection over eight and a half months. In addition to the sensor data, we collected their nutrition, training, and well-being data. The availability of nutrition data with many other important objectives and subjective longitudinal data streams may facilitate research related to food for a healthy lifestyle. Thus, we present a sport, nutrition, and lifestyle logging dataset called ScopeSense from two individuals and discuss its potential use. The dataset is fully open for researchers, and we consider this study as a potential starting point for developing methods to collect and create knowledge for a larger cohort of people.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2023 |
| Conference Name | International Conference on Multimedia Modeling (MMM) |
| Volume | 13833 |
| Pagination | 502 - 514 |
| Publisher | Springer International Publishing |
| Place Published | Cham |
| ISBN Number | 978-3-031-27076-5 |
| ISSN Number | 0302-9743 |
| URL | https://link.springer.com/content/pdf/10.1007/978-3-031-27077-2https://l... |
| DOI | 10.1007/978-3-031-27077-210.1007/978-3-031-27077-2_39 |
Journal Article
ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks
Information Sciences (2022).Status: Published
ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Information Sciences |
| Publisher | arXiv |
DPER: Direct Parameter Estimation for Randomly missing data
Knowledge-Based Systems 240 (2022): 108082.Status: Published
DPER: Direct Parameter Estimation for Randomly missing data
{Parameter estimation is an important problem with applications in discriminant analysis, hypothesis testing, etc. Yet, when there are missing values in the data sets, commonly used imputation-based techniques are usually needed before further parameter estimation since works in direct parameter estimation exists in only limited settings. Unfortunately, such two-step procedures (imputation-parameter estimation) can be computationally expensive. Therefore, it motivates us to propose novel algorithms that directly find the maximum likelihood estimates (MLEs) for an arbitrary one-class/multiple-class randomly missing data set under some mild assumptions. Furthermore, due to the direct computation, our algorithms do not require multiple iterations through the data, thus promising to be less time-consuming while maintaining superior estimation performance than state-of-the-art methods under comparisons. We validate these claims by empirical results on various data sets of different sizes.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Knowledge-Based Systems |
| Volume | 240 |
| Pagination | 108082 |
| Publisher | Elsevier |
| ISSN | 0950-7051 |
| Keywords | MLEs, parameter estimation, Randomly missing data |
| URL | https://www.sciencedirect.com/science/article/pii/S0950705121011540 |
| DOI | 10.1016/j.knosys.2021.108082 |
TATL: Task Agnostic Transfer Learning for Skin Attributes Detection
Medical Image Analysis (2022).Status: Published
TATL: Task Agnostic Transfer Learning for Skin Attributes Detection
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Medical Image Analysis |
| Publisher | Medical Image Analysis |
Miscellaneous
FinNet: Solving Time-Independent Differential Equations with Finite Difference Neural Network
arXiv, 2022.Status: Submitted
FinNet: Solving Time-Independent Differential Equations with Finite Difference Neural Network
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Miscellaneous |
| Year of Publication | 2022 |
| Publisher | arXiv |
Proceedings, refereed
Parallel feature selection based on the trace ratio criterion
In International Joint Conference on Neural Networks (IJCNN). IEEE, 2022.Status: Published
Parallel feature selection based on the trace ratio criterion
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | International Joint Conference on Neural Networks (IJCNN) |
| Publisher | IEEE |
Unequal Covariance Awareness for Fisher Discriminant Analysis and Its Variants in Classification
In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022.Status: Published
Unequal Covariance Awareness for Fisher Discriminant Analysis and Its Variants in Classification
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2022 |
| Conference Name | 2022 International Joint Conference on Neural Networks (IJCNN) |
| Pagination | 1-8 |
| Publisher | IEEE |
Journal Article
EPEM: Efficient Parameter Estimation for Multiple Class Monotone Missing Data
Information Sciences (2021).Status: Published
EPEM: Efficient Parameter Estimation for Multiple Class Monotone Missing Data
The problem of monotone missing data has been broadly studied during the last two decades and has many applications in various fields such as bioinformatics or statistics. Commonly used imputation techniques require multiple iterations through the data before yielding convergence. Moreover, those approaches may introduce extra noises and biases to the subsequent modeling. In this work, we derive exact formulas and propose a novel algorithm to compute the maximum likelihood estimators (MLEs) of a multiple class, monotone missing dataset when all the covariance matrices of all categories are assumed to be equal, namely Efficient Parameter Estimation for Multiple Class Monotone Missing Data (EPEM). We then illustrate an application of our proposed methods in Linear Discriminant Analysis (LDA). As the computation is exact, our EPEM algorithm does not require multiple iterations through the data as other imputation approaches, thus promising to handle much less time-consuming than other methods. This effectiveness was validated by empirical results when EPEM reduced the error rates significantly and required a short computation time compared to several imputation-based approaches. We also release all codes and data of our experiments in a GitHub repository to contribute to the research community related to this problem.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Information Sciences |
| Publisher | Elsevier |
Proceedings, non-refereed
Deep Matrix Tri-Factorization:Mining Vertex-wise Interactions in Multi-Space Attributed Graphs
In SIAM International Conference on Data Mining, 2020.Status: Published
Deep Matrix Tri-Factorization:Mining Vertex-wise Interactions in Multi-Space Attributed Graphs
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, non-refereed |
| Year of Publication | 2020 |
| Conference Name | SIAM International Conference on Data Mining |