765 citations · 1.5k across the 62 of their papers we have counts for
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Locally Linear Embedding and its Variants: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
This is a tutorial and survey paper for Locally Linear Embedding (LLE) and its variants. The idea of LLE is fitting the local structure of manifold in the embedding space. In this…
Sampling Algorithms, from Survey Sampling to Monte Carlo Methods: Tutorial and Literature Review
Benyamin Ghojogh, Hadi Nekoei, Aydin Ghojogh +2
This paper is a tutorial and literature review on sampling algorithms. We have two main types of sampling in statistics. The first type is survey sampling which draws samples from…
Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling
Parisa Abdolrahim Poorheravi, Benyamin Ghojogh, Vincent Gaudet +2
Metric learning is one of the techniques in manifold learning with the goal of finding a projection subspace for increasing and decreasing the inter- and intra-class variances, res…
Integration of Roadside Camera Images and Weather Data for Monitoring Winter Road Surface Conditions
Juan Carrillo, Mark Crowley
During the winter season, real-time monitoring of road surface conditions is critical for the safety of drivers and road maintenance operations. Previous research has evaluated the…
Design of Efficient Deep Learning models for Determining Road Surface Condition from Roadside Camera Images and Weather Data
Juan Carrillo, Mark Crowley, Guangyuan Pan +1
Road maintenance during the Winter season is a safety critical and resource demanding operation. One of its key activities is determining road surface condition (RSC) in order to p…
Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees
Juan Carrillo, Daniel Garijo, Mark Crowley +3
Climate science is critical for understanding both the causes and consequences of changes in global temperatures and has become imperative for decisive policy-making. However, clim…
Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
Multidimensional Scaling (MDS) is one of the first fundamental manifold learning methods. It can be categorized into several methods, i.e., classical MDS, kernel classical MDS, met…
Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
Stochastic Neighbor Embedding (SNE) is a manifold learning and dimensionality reduction method with a probabilistic approach. In SNE, every point is consider to be the neighbor of…
Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem
Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray +2
Variants of Triplet networks are robust entities for learning a discriminative embedding subspace. There exist different triplet mining approaches for selecting the most suitable t…
Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches
Milad Sikaroudi, Benyamin Ghojogh, Amir Safarpoor +3
We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100,000 patches. We consider the extreme, i.e., farthest an…
Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action Recognition
Benyamin Ghojogh, Fakhri Karray, Mark Crowley
Human action recognition is one of the important fields of computer vision and machine learning. Although various methods have been proposed for 3D action recognition, some of whic…
Quantile-Quantile Embedding for Distribution Transformation and Manifold Embedding with Ability to Choose the Embedding Distribution
Benyamin Ghojogh, Fakhri Karray, Mark Crowley
We propose a new embedding method, named Quantile-Quantile Embedding (QQE), for distribution transformation and manifold embedding with the ability to choose the embedding distribu…
Active Measure Reinforcement Learning for Observation Cost Minimization
Colin Bellinger, Rory Coles, Mark Crowley +1
Standard reinforcement learning (RL) algorithms assume that the observation of the next state comes instantaneously and at no cost. In a wide variety of sequential decision making…
Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study
Milad Sikaroudi, Amir Safarpoor, Benyamin Ghojogh +3
As many algorithms depend on a suitable representation of data, learning unique features is considered a crucial task. Although supervised techniques using deep neural networks hav…
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment
Colin Bellinger, Rory Coles, Mark Crowley +1
Reinforcement learning (RL) has been demonstrated to have great potential in many applications of scientific discovery and design. Recent work includes, for example, the design of…
Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
Benyamin Ghojogh, Milad Sikaroudi, Sobhan Shafiei +3
Siamese neural network is a very powerful architecture for both feature extraction and metric learning. It usually consists of several networks that share weights. The Siamese conc…
Backprojection for Training Feedforward Neural Networks in the Input and Feature Spaces
Benyamin Ghojogh, Fakhri Karray, Mark Crowley
After the tremendous development of neural networks trained by backpropagation, it is a good time to develop other algorithms for training neural networks to gain more insights int…
Anomaly Detection and Prototype Selection Using Polyhedron Curvature
Benyamin Ghojogh, Fakhri Karray, Mark Crowley
We propose a novel approach to anomaly detection called Curvature Anomaly Detection (CAD) and Kernel CAD based on the idea of polyhedron curvature. Using the nearest neighbors for…
Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders
Benyamin Ghojogh, Fakhri Karray, Mark Crowley
Generative models and inferential autoencoders mostly make use of norm in their optimization objectives. In order to generate perceptually better images, this short paper…
Weighted Fisher Discriminant Analysis in the Input and Feature Spaces
Benyamin Ghojogh, Milad Sikaroudi, H. R. Tizhoosh +2
Fisher Discriminant Analysis (FDA) is a subspace learning method which minimizes and maximizes the intra- and inter-class scatters of data, respectively. Although, in FDA, all the…
Distributed Nonlinear Model Predictive Control and Metric Learning for Heterogeneous Vehicle Platooning with Cut-in/Cut-out Maneuvers
Mohammad Hossein Basiri, Benyamin Ghojogh, Nasser L. Azad +3
Vehicle platooning has been shown to be quite fruitful in the transportation industry to enhance fuel economy, road throughput, and driving comfort. Model Predictive Control (MPC)…
Isolation Mondrian Forest for Batch and Online Anomaly Detection
Haoran Ma, Benyamin Ghojogh, Maria N. Samad +2
We propose a new method, named isolation Mondrian forest (iMondrian forest), for batch and online anomaly detection. The proposed method is a novel hybrid of isolation forest and M…
A review of machine learning applications in wildfire science and management
Piyush Jain, Sean C P Coogan, Sriram Ganapathi Subramanian +3
Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the fi…