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Found 61 papers, 41 papers with code
Probabilistic Classification by Density Estimation Using Gaussian Mixture Model and Masked Autoregressive Flow
A family of density estimators is mixture models, such as Gaussian Mixture Model (GMM) by expectation maximization.
On Philomatics and Psychomatics for Combining Philosophy and Psychology with Mathematics
We enumerate various examples for philomatics and psychomatics, some of which are explained in more depth.
Recurrent Neural Networks and Long Short-Term Memory Networks: Tutorial and Survey
Then, we introduce LSTM gates and cells, history and variants of LSTM, and Gated Recurrent Units (GRU).
Gravitational Dimensionality Reduction Using Newtonian Gravity and Einstein's General Relativity
Due to the effectiveness of using machine learning in physics, it has been widely received increased attention in the literature.
Affective Manifolds: Modeling Machine's Mind to Like, Dislike, Enjoy, Suffer, Worry, Fear, and Feel Like A Human
More affective manifolds in the machine's mind can make it more realistic and effective.
Theoretical Connection between Locally Linear Embedding, Factor Analysis, and Probabilistic PCA
Locally Linear Embedding (LLE) is a nonlinear spectral dimensionality reduction and manifold learning method.
On Manifold Hypothesis: Hypersurface Submanifold Embedding Using Osculating Hyperspheres
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 $\{1, 2, \dots, d-1\}$.
Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey
In deep learning methods, we first introduce reconstruction autoencoders and supervised loss functions for metric learning.
Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey
Finally, we explain the autoencoders based on adversarial learning including adversarial autoencoder, PixelGAN, and implicit autoencoder.
Sufficient Dimension Reduction for High-Dimensional Regression and Low-Dimensional Embedding: Tutorial and Survey
Finally, we explain Kernel Dimension Reduction (KDR) both for supervised and unsupervised learning.
KKT Conditions, First-Order and Second-Order Optimization, and Distributed Optimization: Tutorial and Survey
Then, we explain second-order methods including Newton's method for unconstrained, equality constrained, and inequality constrained problems....
Vector Transport Free Riemannian LBFGS for Optimization on Symmetric Positive Definite Matrix Manifolds
Riemannian LBFGS (RLBFGS) is an extension of this method to Riemannian manifolds.
Uniform Manifold Approximation and Projection (UMAP) and its Variants: Tutorial and Survey
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.
Johnson-Lindenstrauss Lemma, Linear and Nonlinear Random Projections, Random Fourier Features, and Random Kitchen Sinks: Tutorial and Survey
This is a tutorial and survey paper on the Johnson-Lindenstrauss (JL) lemma and linear and nonlinear random projections.
Restricted Boltzmann Machine and Deep Belief Network: Tutorial and Survey
Then, we introduce the structures of BM and RBM.
Generative locally linear embedding: A module for manifold unfolding and visualization
One can unfold the nonlinear manifold of a dataset for low-dimensional visualization and feature extraction.
Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Survey
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.
Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey
We start with reviewing the history of kernels in functional analysis and machine learning.
Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey
Versions of graph embedding are then explained which are generalized versions of Laplacian eigenmap and locality preserving projection.
Generative Locally Linear Embedding
In this work, we propose two novel generative versions of LLE, named Generative LLE (GLLE), whose linear reconstruction steps are stochastic rather than deterministic.
Magnification Generalization for Histopathology Image Embedding
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
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Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey
Finally, VAE is explained where the encoder, decoder and sampling from the latent space are introduced.
Locally Linear Embedding and its Variants: Tutorial and Survey
In this paper, we first cover LLE, kernel LLE, inverse LLE, and feature fusion with LLE.
Attention Mechanism, Transformers, BERT, and GPT: Tutorial and Survey
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.
Sampling Algorithms, from Survey Sampling to Monte Carlo Methods: Tutorial and Literature Review
This paper is a tutorial and literature review on sampling algorithms.
Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling
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.
Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey
Stochastic Neighbor Embedding (SNE) is a manifold learning and dimensionality reduction method with a probabilistic approach.
Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and Survey
Then, Sammon mapping, Isomap, and kernel Isomap are explained.
Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem
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
Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches
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
Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action Recognition
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.
Quantile-Quantile Embedding for Distribution Transformation and Manifold Embedding with Ability to Choose the Embedding Distribution
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.
Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study
In this work, we explored the performance of a deep neural network and triplet loss in the area of representation learning.
Anomaly Detection and Prototype Selection Using Polyhedron Curvature
We propose a novel approach to anomaly detection called Curvature Anomaly Detection (CAD) and Kernel CAD based on the idea of polyhedron curvature.
Backprojection for Training Feedforward Neural Networks in the Input and Feature Spaces
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.
Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
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
Weighted Fisher Discriminant Analysis in the Input and Feature Spaces
We also propose a weighted FDA in the feature space to establish a weighted kernel FDA for both existing and newly proposed weights.
Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders
Generative models and inferential autoencoders mostly make use of $\ell_2$ norm in their optimization objectives.
Isolation Mondrian Forest for Batch and Online Anomaly Detection
We propose a new method, named isolation Mondrian forest (iMondrian forest), for batch and online anomaly detection.
Hidden Markov Model: Tutorial
Then, we explain how to train HMM using EM and the Baum-Welch algorithm.
Distributed Voting in Beep Model
For the second algorithm, we show that it returns the correct output with high probability.
Distributed Voting
Distributed, Parallel, and Cluster Computing
Quantitative Methods
Roweis Discriminant Analysis: A Generalized Subspace Learning Method
We also propose kernel RDA, generalizing kernel PCA, kernel SPCA, and kernel FDA, using both dual RDA and representation theory.
Quantized Fisher Discriminant Analysis
This paper proposes a new subspace learning method, named Quantized Fisher Discriminant Analysis (QFDA), which makes use of both machine learning and information theory.
Principal Component Analysis Using Structural Similarity Index for Images
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.
Locally Linear Image Structural Embedding for Image Structure Manifold Learning
We propose a new manifold learning method, Locally Linear Image Structural Embedding (LLISE), and kernel LLISE for learning this manifold.
Fisher and Kernel Fisher Discriminant Analysis: Tutorial
This is a detailed tutorial paper which explains the Fisher discriminant Analysis (FDA) and kernel FDA.
Linear and Quadratic Discriminant Analysis: Tutorial
We also prove that LDA and Fisher discriminant analysis are equivalent.
Unsupervised and Supervised Principal Component Analysis: Tutorial
Then, PCA with singular value decomposition, dual PCA, and kernel PCA are covered.
The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial
The upper bound on the generalization error of boosting is also provided to show why boosting prevents from overfitting.
Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review
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.
Instance Ranking and Numerosity Reduction Using Matrix Decomposition and Subspace Learning
Using this similarity measure, we propose several related algorithms for ranking data instances and performing numerosity reduction.
Eigenvalue and Generalized Eigenvalue Problems: Tutorial
This paper is a tutorial for eigenvalue and generalized eigenvalue problems.
BIG-bench Machine Learning
Matrix Factorization / Decomposition
Artificial Counselor System for Stock Investment
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
Addressing the Mystery of Population Decline of the Rose-Crested Blue Pipit in a Nature Preserve using Data Visualization
Two main methods for exploring patterns in data are data visualization and machine learning.
Data Visualization
Applications
Fitting A Mixture Distribution to Data: Tutorial
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.
Tree-Based Optimization: A Meta-Algorithm for Metaheuristic Optimization
The experimental results on several well-known benchmarks show the outperforming performance of TBO algorithm in finding the global solution.
Pontogammarus Maeoticus Swarm Optimization: A Metaheuristic Optimization Algorithm
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.
Fisherposes for Human Action Recognition Using Kinect Sensor Data
The proposed regularized Mahalanobis distance metric is used in order to recognize both the involuntary and highly made-up actions at the same time.
Pixel-Level Alignment of Facial Images for High Accuracy Recognition Using Ensemble of Patches
The geometry alignment is performed pixel-wise, i. e., every pixel of the face is corresponded to a pixel of the reference face.
A Fusion-based Gender Recognition Method Using Facial Images
This paper proposes a fusion-based gender recognition method which uses facial images as input.
Recognizing Involuntary Actions from 3D Skeleton Data Using Body States
This method introduces the definition of body states and then every action is modeled as a sequence of these states.
