1 code implementation • 16 Oct 2023 • Benyamin Ghojogh, Milad Amir Toutounchian

A family of density estimators is mixture models, such as Gaussian Mixture Model (GMM) by expectation maximization.

no code implementations • 26 Aug 2023 • Benyamin Ghojogh, Morteza Babaie

We enumerate various examples for philomatics and psychomatics, some of which are explained in more depth.

no code implementations • 22 Apr 2023 • Benyamin Ghojogh, Ali Ghodsi

Then, we introduce LSTM gates and cells, history and variants of LSTM, and Gated Recurrent Units (GRU).

1 code implementation • 30 Oct 2022 • Benyamin Ghojogh, Smriti Sharma

Due to the effectiveness of using machine learning in physics, it has been widely received increased attention in the literature.

1 code implementation • 29 Aug 2022 • Benyamin Ghojogh

More affective manifolds in the machine's mind can make it more realistic and effective.

no code implementations • 25 Mar 2022 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Locally Linear Embedding (LLE) is a nonlinear spectral dimensionality reduction and manifold learning method.

no code implementations • 3 Feb 2022 • Benyamin Ghojogh, Fakhri Karray, Mark Crowley

Using an induction in a pyramid structure, we also extend the embedding dimensionality to lower embedding dimensionalities to show the validity of manifold hypothesis for embedding dimensionalities $\{1, 2, \dots, d-1\}$.

no code implementations • 23 Jan 2022 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

In deep learning methods, we first introduce reconstruction autoencoders and supervised loss functions for metric learning.

no code implementations • 26 Nov 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Finally, we explain the autoencoders based on adversarial learning including adversarial autoencoder, PixelGAN, and implicit autoencoder.

no code implementations • 18 Oct 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Finally, we explain Kernel Dimension Reduction (KDR) both for supervised and unsupervised learning.

no code implementations • 5 Oct 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Then, we explain second-order methods including Newton's method for unconstrained, equality constrained, and inequality constrained problems....

1 code implementation • 25 Aug 2021 • Reza Godaz, Benyamin Ghojogh, Reshad Hosseini, Reza Monsefi, Fakhri Karray, Mark Crowley

Riemannian LBFGS (RLBFGS) is an extension of this method to Riemannian manifolds.

no code implementations • 25 Aug 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

We start with UMAP algorithm where we explain probabilities of neighborhood in the input and embedding spaces, optimization of cost function, training algorithm, derivation of gradients, and supervised and semi-supervised embedding by UMAP.

no code implementations • 9 Aug 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

This is a tutorial and survey paper on the Johnson-Lindenstrauss (JL) lemma and linear and nonlinear random projections.

no code implementations • 26 Jul 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Then, we introduce the structures of BM and RBM.

1 code implementation • Software Impacts 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

One can unfold the nonlinear manifold of a dataset for low-dimensional visualization and feature extraction.

no code implementations • 29 Jun 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

This is a tutorial and survey paper on unification of spectral dimensionality reduction methods, kernel learning by Semidefinite Programming (SDP), Maximum Variance Unfolding (MVU) or Semidefinite Embedding (SDE), and its variants.

no code implementations • 15 Jun 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

We start with reviewing the history of kernels in functional analysis and machine learning.

no code implementations • 3 Jun 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Versions of graph embedding are then explained which are generalized versions of Laplacian eigenmap and locality preserving projection.

1 code implementation • 4 Apr 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

In this work, we propose two novel generative versions of LLE, named Generative LLE (GLLE), whose linear reconstruction steps are stochastic rather than deterministic.

1 code implementation • 18 Jan 2021 • Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray, Mark Crowley, H. R. Tizhoosh

However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level.

Breast Cancer Histology Image Classification
Classification Of Breast Cancer Histology Images
**+3**

no code implementations • 4 Jan 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Finally, VAE is explained where the encoder, decoder and sampling from the latent space are introduced.

1 code implementation • 22 Nov 2020 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

In this paper, we first cover LLE, kernel LLE, inverse LLE, and feature fusion with LLE.

no code implementations • 17 Nov 2020 • Benyamin Ghojogh, Ali Ghodsi

Thereafter, we introduce the Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT) as the stacks of encoders and decoders of transformer, respectively.

no code implementations • 2 Nov 2020 • Benyamin Ghojogh, Hadi Nekoei, Aydin Ghojogh, Fakhri Karray, Mark Crowley

This paper is a tutorial and literature review on sampling algorithms.

1 code implementation • 29 Sep 2020 • Parisa Abdolrahim Poorheravi, Benyamin Ghojogh, Vincent Gaudet, Fakhri Karray, Mark Crowley

Many triplet mining methods have been developed for Siamese networks; however, these techniques have not been applied on the triplets of large margin metric learning for nearest neighbor classification.

1 code implementation • 22 Sep 2020 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Stochastic Neighbor Embedding (SNE) is a manifold learning and dimensionality reduction method with a probabilistic approach.

1 code implementation • 17 Sep 2020 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Then, Sammon mapping, Isomap, and kernel Isomap are explained.

1 code implementation • 10 Jul 2020 • Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray, Mark Crowley, H. R. Tizhoosh

However, sampling from stochastic distributions of data rather than sampling merely from the existing embedding instances can provide more discriminative information.

Dimensionality Reduction
Histopathological Image Classification
**+1**

1 code implementation • 4 Jul 2020 • Milad Sikaroudi, Benyamin Ghojogh, Amir Safarpoor, Fakhri Karray, Mark Crowley, H. R. Tizhoosh

We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100, 000 patches.

Dimensionality Reduction
Histopathological Image Classification
**+1**

1 code implementation • 28 Jun 2020 • Benyamin Ghojogh, Fakhri Karray, Mark Crowley

Although various methods have been proposed for 3D action recognition, some of which are basic and some use deep learning, the need of basic methods based on generalized eigenvalue problem is sensed for action recognition.

1 code implementation • 19 Jun 2020 • 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 distribution.

1 code implementation • 10 May 2020 • Milad Sikaroudi, Amir Safarpoor, Benyamin Ghojogh, Sobhan Shafiei, Mark Crowley, H. R. Tizhoosh

In this work, we explored the performance of a deep neural network and triplet loss in the area of representation learning.

1 code implementation • 5 Apr 2020 • 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 into networks.

1 code implementation • 5 Apr 2020 • Benyamin Ghojogh, Milad Sikaroudi, Sobhan Shafiei, H. R. Tizhoosh, Fakhri Karray, Mark Crowley

The FDT and FDC loss functions are designed based on the statistical formulation of the Fisher Discriminant Analysis (FDA), which is a linear subspace learning method.

Classification Of Breast Cancer Histology Images
Dimensionality Reduction
**+3**

1 code implementation • 5 Apr 2020 • 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.

no code implementations • 4 Apr 2020 • Benyamin Ghojogh, Fakhri Karray, Mark Crowley

Generative models and inferential autoencoders mostly make use of $\ell_2$ norm in their optimization objectives.

1 code implementation • 4 Apr 2020 • Benyamin Ghojogh, Milad Sikaroudi, H. R. Tizhoosh, Fakhri Karray, Mark Crowley

We also propose a weighted FDA in the feature space to establish a weighted kernel FDA for both existing and newly proposed weights.

1 code implementation • 8 Mar 2020 • Haoran Ma, Benyamin Ghojogh, Maria N. Samad, Dongyu Zheng, Mark Crowley

We propose a new method, named isolation Mondrian forest (iMondrian forest), for batch and online anomaly detection.

no code implementations • engrXiv 2019 • Benyamin Ghojogh, Fakhri Karray, Mark Crowley

Then, we explain how to train HMM using EM and the Baum-Welch algorithm.

1 code implementation • Signal Processing, Elsevier 2020 • Benyamin Ghojogh, Saber Salehkaleybar

For the second algorithm, we show that it returns the correct output with high probability.

Distributed Voting Distributed, Parallel, and Cluster Computing Quantitative Methods

1 code implementation • 11 Oct 2019 • Benyamin Ghojogh, Fakhri Karray, Mark Crowley

We also propose kernel RDA, generalizing kernel PCA, kernel SPCA, and kernel FDA, using both dual RDA and representation theory.

1 code implementation • 6 Sep 2019 • Benyamin Ghojogh, Ali Saheb Pasand, Fakhri Karray, Mark Crowley

This paper proposes a new subspace learning method, named Quantized Fisher Discriminant Analysis (QFDA), which makes use of both machine learning and information theory.

1 code implementation • 25 Aug 2019 • Benyamin Ghojogh, Fakhri Karray, Mark Crowley

Despite the advances of deep learning in specific tasks using images, the principled assessment of image fidelity and similarity is still a critical ability to develop.

1 code implementation • 25 Aug 2019 • Benyamin Ghojogh, Fakhri Karray, Mark Crowley

We propose a new manifold learning method, Locally Linear Image Structural Embedding (LLISE), and kernel LLISE for learning this manifold.

2 code implementations • 22 Jun 2019 • Benyamin Ghojogh, Fakhri Karray, Mark Crowley

This is a detailed tutorial paper which explains the Fisher discriminant Analysis (FDA) and kernel FDA.

1 code implementation • 1 Jun 2019 • Benyamin Ghojogh, Mark Crowley

We also prove that LDA and Fisher discriminant analysis are equivalent.

1 code implementation • 1 Jun 2019 • Benyamin Ghojogh, Mark Crowley

Then, PCA with singular value decomposition, dual PCA, and kernel PCA are covered.

no code implementations • 28 May 2019 • Benyamin Ghojogh, Mark Crowley

The upper bound on the generalization error of boosting is also provided to show why boosting prevents from overfitting.

2 code implementations • 7 May 2019 • Benyamin Ghojogh, Maria N. Samad, Sayema Asif Mashhadi, Tania Kapoor, Wahab Ali, Fakhri Karray, Mark Crowley

Pattern analysis often requires a pre-processing stage for extracting or selecting features in order to help the classification, prediction, or clustering stage discriminate or represent the data in a better way.

1 code implementation • Canadian Conference on Artificial Intelligence 2019 • Benyamin Ghojogh, Mark Crowley

Using this similarity measure, we propose several related algorithms for ranking data instances and performing numerosity reduction.

1 code implementation • 25 Mar 2019 • Benyamin Ghojogh, Fakhri Karray, Mark Crowley

This paper is a tutorial for eigenvalue and generalized eigenvalue problems.

BIG-bench Machine Learning Matrix Factorization / Decomposition

2 code implementations • Proceedings of the AAAI Conference on Artificial Intelligence 2019 • Hadi NekoeiQachkanloo, Benyamin Ghojogh, Ali Saheb Pasand, Mark Crowley

This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment.

Portfolio Optimization
Stock Prediction
**+1**
General Finance
Computational Engineering, Finance, and Science
General Economics
Economics

1 code implementation • 3 Mar 2019 • Benyamin Ghojogh, Mark Crowley, Fakhri Karray

Two main methods for exploring patterns in data are data visualization and machine learning.

Data Visualization Applications

1 code implementation • 20 Jan 2019 • Benyamin Ghojogh, Aydin Ghojogh, Mark Crowley, Fakhri Karray

In explaining the main algorithm, first, fitting a mixture of two distributions is detailed and examples of fitting two Gaussians and Poissons, respectively for continuous and discrete cases, are introduced.

1 code implementation • 25 Sep 2018 • Benyamin Ghojogh, Saeed Sharifian, Hoda Mohammadzade

The experimental results on several well-known benchmarks show the outperforming performance of TBO algorithm in finding the global solution.

1 code implementation • 5 Jul 2018 • Benyamin Ghojogh, Saeed Sharifian

In this algorithm, global optima is modeled as sea edge (coast) to which Gammarus creatures are willing to move in order to rest from sea waves and forage in sand.

1 code implementation • 15 Feb 2018 • Benyamin Ghojogh, Hoda Mohammadzade, Mozhgan Mokari

The proposed regularized Mahalanobis distance metric is used in order to recognize both the involuntary and highly made-up actions at the same time.

1 code implementation • 7 Feb 2018 • Hoda Mohammadzade, Amirhossein Sayyafan, Benyamin Ghojogh

The geometry alignment is performed pixel-wise, i. e., every pixel of the face is corresponded to a pixel of the reference face.

1 code implementation • 17 Nov 2017 • Benyamin Ghojogh, Saeed Bagheri Shouraki, Hoda Mohammadzade, Ensieh Iranmehr

This paper proposes a fusion-based gender recognition method which uses facial images as input.

1 code implementation • 21 Aug 2017 • Mozhgan Mokari, Hoda Mohammadzade, Benyamin Ghojogh

This method introduces the definition of body states and then every action is modeled as a sequence of these states.

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