Seminar: Fahad Sohrab

“Subspace Learning for One-Class Classification”
Wednesday, Nov. 2 at 3:30pm
Larsen Hall 234 [ map ]


Machine learning deals with discovering the knowledge that governs the learning process. The science of machine learning helps create techniques that enhance the capabilities of a system through the use of data. Typical machine learning techniques identify or predict different patterns in the data. In classifcation tasks, a machine learning model is trained using some training data to identify the unknown function that maps the input data to the output labels. The classifcation task gets challenging if the data from some categories are either unavailable or so diverse that they cannot be modelled statistically. For example, to train a model for anomaly detection, it is usually challenging to collect anomalous data for training, but the normal data is available in abundance. In such cases, it is possible to use One-Class Classifcation (OCC) techniques where the model is trained by using data only from one class. OCC algorithms are practical in situations where it is vital to identify one of the categories, but the examples from that specific category are scarce. Numerous OCC techniques have been proposed in the literature that model the data in the given feature space; however, such data can be high-dimensional or may not provide discriminative information for classification. In order to avoid the curse of dimensionality, standard dimensionality reduction techniques are commonly used as a preprocessing step in many machine learning algorithms. In this talk we present a new paradigm that jointly optimizes a subspace and data description for one-class classifcation via Support Vector Data Description (SVDD).


Dr. Fahad Sohrab (Member, IEEE) received his BS degree in telecommunication engineering from the National University of Computer and Emerging Sciences, Peshawar, Pakistan, in 2012. He received his MS degree from Sabanci University, Istanbul, Turkey, in 2016 and his Ph.D. from Tampere University, Finland, in 2022. During his BS studies, he received three silver medals, six certificates of the dean’s honor list, and graduated with honors (cum laude). During his BS studies, he was selected by the ministry of science & technology Pakistan to represent Pakistan at the 54th London International Youth Science Forum held at Imperial College London, United Kingdom, in 2012. In the fall of 2013, he joined the Computer Vision and Pattern Analysis Laboratory at Sabanci University Istanbul for his master’s studies. He was affiliated with the Pattern Recognition Laboratory, Delft University of Technology, Netherlands, during the second year of his master’s studies. He received a fully funded scholarship for his MS studies. He was awarded the best teaching assistant award for his performance during his teaching assistantship duties. In May 2017, he joined Signal Analysis and Machine Intelligence research group at Tampere University Finland. During his Ph.D. studies, he worked on various projects. He developed several novel machine-learning methods published in highly reputable journals and conferences. In 2020, he received an award from Nokia Foundation, and in 2022 from Finnish Foundation for Technology Promotion for his research. Currently, he is a Postdoctoral Research Fellow at the SAMI research group in the Department of Computing Sciences at Tampere University, Finland. His research interests include machine learning, pattern recognition, subspace learning, and one-class classification.