249 citations · 1.5k across the 70 of their papers we have counts for
20 papers · 1 filter
Credit Card Fraud Detection with Subspace Learning-based One-Class Classification
Zaffar Zaffar, Fahad Sohrab, Juho Kanniainen +1
In an increasingly digitalized commerce landscape, the proliferation of credit card fraud and the evolution of sophisticated fraudulent techniques have led to substantial financial…
One-Class Classification for Intrusion Detection on Vehicular Networks
Jake Guidry, Fahad Sohrab, Raju Gottumukkala +2
Controller Area Network bus systems within vehicular networks are not equipped with the tools necessary to ward off and protect themselves from modern cyber-security threats. Work…
Newton Method-based Subspace Support Vector Data Description
Fahad Sohrab, Firas Laakom, Moncef Gabbouj
In this paper, we present an adaptation of Newton's method for the optimization of Subspace Support Vector Data Description (S-SVDD). The objective of S-SVDD is to map the original…
Graph-Embedded Subspace Support Vector Data Description
Fahad Sohrab, Alexandros Iosifidis, Moncef Gabbouj +1
In this paper, we propose a novel subspace learning framework for one-class classification. The proposed framework presents the problem in the form of graph embedding. It includes…
Graph Embedding with Data Uncertainty
Firas Laakom, Jenni Raitoharju, Nikolaos Passalis +2
spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines. The main aim is to learn a meaningful low dimensional embedding of the data…
Attention-based Neural Bag-of-Features Learning for Sequence Data
Dat Thanh Tran, Nikolaos Passalis, Anastasios Tefas +2
In this paper, we propose 2D-Attention (2DA), a generic attention formulation for sequence data, which acts as a complementary computation block that can detect and focus on releva…
Self-Organized Operational Neural Networks with Generative Neurons
Serkan Kiranyaz, Junaid Malik, Habib Ben Abdallah +3
Operational Neural Networks (ONNs) have recently been proposed to address the well-known limitations and drawbacks of conventional Convolutional Neural Networks (CNNs) such as netw…
Saliency-based Weighted Multi-label Linear Discriminant Analysis
Lei Xu, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a new variant of Linear Discriminant Analysis (LDA) to solve multi-label classification tasks. The proposed method is based on a probabilistic model for d…
Not all domains are equally complex: Adaptive Multi-Domain Learning
Ali Senhaji, Jenni Raitoharju, Moncef Gabbouj +1
Deep learning approaches are highly specialized and require training separate models for different tasks. Multi-domain learning looks at ways to learn a multitude of different task…
Ellipsoidal Subspace Support Vector Data Description
Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification. The proposed method iteratively transforms data…
Incremental Fast Subclass Discriminant Analysis
Kateryna Chumachenko, Jenni Raitoharju, Moncef Gabbouj +1
This paper proposes an incremental solution to Fast Subclass Discriminant Analysis (fastSDA). We present an exact and an approximate linear solution, along with an approximate kern…
Subset Sampling For Progressive Neural Network Learning
Dat Thanh Tran, Moncef Gabbouj, Alexandros Iosifidis
Progressive Neural Network Learning is a class of algorithms that incrementally construct the network's topology and optimize its parameters based on the training data. While this…
Speed-up and multi-view extensions to Subclass Discriminant Analysis
Kateryna Chumachenko, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a speed-up approach for subclass discriminant analysis and formulate a novel efficient multi-view solution to it. The speed-up approach is developed based…
Multimodal Subspace Support Vector Data Description
Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively tran…
Data-driven Neural Architecture Learning For Financial Time-series Forecasting
Dat Thanh Tran, Juho Kanniainen, Moncef Gabbouj +1
Forecasting based on financial time-series is a challenging task since most real-world data exhibits nonstationary property and nonlinear dependencies. In addition, different data…
Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data
Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen +2
Time series forecasting is a crucial component of many important applications, ranging from forecasting the stock markets to energy load prediction. The high-dimensionality, veloci…
Using Deep Learning for price prediction by exploiting stationary limit order book features
Avraam Tsantekidis, Nikolaos Passalis, Anastasios Tefas +3
The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems. The field of quantitative analysis has been slowl…
Compressively Sensed Image Recognition
Aysen Degerli, Sinem Aslan, Mehmet Yamac +2
Compressive Sensing (CS) theory asserts that sparse signal reconstruction is possible from a small number of linear measurements. Although CS enables low-cost linear sampling, it r…
Deep Multi-view Learning to Rank
Guanqun Cao, Alexandros Iosifidis, Moncef Gabbouj +2
We study the problem of learning to rank from multiple information sources. Though multi-view learning and learning to rank have been studied extensively leading to a wide range of…
Video Ladder Networks
Francesco Cricri, Xingyang Ni, Mikko Honkala +2
We present the Video Ladder Network (VLN) for efficiently generating future video frames. VLN is a neural encoder-decoder model augmented at all layers by both recurrent and feedfo…