Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimation

Learning generative probabilistic models is a core problem in machine learning, which presents significant challenges due to the curse of dimensionality. This paper proposes a joint dimensionality reduction and non-parametric density estimation framework, using a novel estimator that can explicitly capture the underlying distribution of appropriate reduced-dimension representations of the input data. The idea is to jointly design a nonlinear dimensionality reducing auto-encoder to model the training data in terms of a parsimonious set of latent random variables, and learn a canonical low-rank tensor model of the joint distribution of the latent variables in the Fourier domain. The proposed latent density model is non-parametric and universal, as opposed to the predefined prior that is assumed in variational auto-encoders. Joint optimization of the auto-encoder and the latent density estimator is pursued via a formulation which learns both by minimizing a combination of the negative log-likelihood in the latent domain and the auto-encoder reconstruction loss. We demonstrate that the proposed model achieves very promising results on toy, tabular, and image datasets on regression tasks, sampling, and anomaly detection

The Role of Synthetic Data in Improving Supervised Learning Methods: The Case of Land Use/Land Cover Classification

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information ManagementIn remote sensing, Land Use/Land Cover (LULC) maps constitute important assets for various applications, promoting environmental sustainability and good resource management. Although, their production continues to be a challenging task. There are various factors that contribute towards the difficulty of generating accurate, timely updated LULC maps, both via automatic or photo-interpreted LULC mapping. Data preprocessing, being a crucial step for any Machine Learning task, is particularly important in the remote sensing domain due to the overwhelming amount of raw, unlabeled data continuously gathered from multiple remote sensing missions. However a significant part of the state-of-the-art focuses on scenarios with full access to labeled training data with relatively balanced class distributions. This thesis focuses on the challenges found in automatic LULC classification tasks, specifically in data preprocessing tasks. We focus on the development of novel Active Learning (AL) and imbalanced learning techniques, to improve ML performance in situations with limited training data and/or the existence of rare classes. We also show that much of the contributions presented are not only successful in remote sensing problems, but also in various other multidisciplinary classification problems. The work presented in this thesis used open access datasets to test the contributions made in imbalanced learning and AL. All the data pulling, preprocessing and experiments are made available at https://github.com/joaopfonseca/publications. The algorithmic implementations are made available in the Python package ml-research at https://github.com/joaopfonseca/ml-research

a literature review

Fonseca, J., & Bacao, F. (2023). Tabular and latent space synthetic data generation: a literature review. Journal of Big Data, 10, 1-37. [115]. https://doi.org/10.1186/s40537-023-00792-7 --- This research was supported by two research grants of the Portuguese Foundation for Science and Technology (“Fundação para a Ciência e a Tecnologia”), references SFRH/BD/151473/2021 and DSAIPA/DS/0116/2019, and by project UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC).The generation of synthetic data can be used for anonymization, regularization, oversampling, semi-supervised learning, self-supervised learning, and several other tasks. Such broad potential motivated the development of new algorithms, specialized in data generation for specific data formats and Machine Learning (ML) tasks. However, one of the most common data formats used in industrial applications, tabular data, is generally overlooked; Literature analyses are scarce, state-of-the-art methods are spread across domains or ML tasks and there is little to no distinction among the main types of mechanism underlying synthetic data generation algorithms. In this paper, we analyze tabular and latent space synthetic data generation algorithms. Specifically, we propose a unified taxonomy as an extension and generalization of previous taxonomies, review 70 generation algorithms across six ML problems, distinguish the main generation mechanisms identified into six categories, describe each type of generation mechanism, discuss metrics to evaluate the quality of synthetic data and provide recommendations for future research. We expect this study to assist researchers and practitioners identify relevant gaps in the literature and design better and more informed practices with synthetic data.publishersversionpublishe

Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GAN

This study presents an adversarial method for anomaly detection in real-world applications, leveraging the power of generative adversarial neural networks (GANs) through cycle consistency in reconstruction error. Previous methods suffer from the high variance between class-wise accuracy which leads to not being applicable for all types of anomalies. The proposed method named RCALAD tries to solve this problem by introducing a novel discriminator to the structure, which results in a more efficient training process. Additionally, RCALAD employs a supplementary distribution in the input space to steer reconstructions toward the normal data distribution, effectively separating anomalous samples from their reconstructions and facilitating more accurate anomaly detection. To further enhance the performance of the model, two novel anomaly scores are introduced. The proposed model has been thoroughly evaluated through extensive experiments on six various datasets, yielding results that demonstrate its superiority over existing state-of-the-art models. The code is readily available to the research community at https://github.com/zahraDehghanian97/RCALAD.Comment: under revision of Applied Soft Computing Journa

Generalized topographic block model

Co-clustering leads to parsimony in data visualisation with a number of parameters dramatically reduced in comparison to the dimensions of the data sample. Herein, we propose a new generalized approach for nonlinear mapping by a re-parameterization of the latent block mixture model. The densities modeling the blocks are in an exponential family such that the Gaussian, Bernoulli and Poisson laws are particular cases. The inference of the parameters is derived from the block expectation–maximization algorithm with a Newton–Raphson procedure at the maximization step. Empirical experiments with textual data validate the interest of our generalized model

Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments

Can Tabular Generative Models Generate Realistic Synthetic Near Infrared Spectroscopic Data?

In this thesis, we evaluated the performance of two generative models, Conditional Tabular Gen- erative Adversarial Network (CTGAN) and Tabular Variational Autoencoder (TVAE), from the open-source library Synthetic Data Vault (SDV), for generating synthetic Near Infrared (NIR) spectral data. The aim was to assess the viability of these models in synthetic data generation for predicting Dry Matter Content (DMC) in the field of NIR spectroscopy. The fidelity and utility of the synthetic data were examined through a series of benchmarks, including statistical comparisons, dimensionality reduction, and machine learning tasks. The results showed that while both CTGAN and TVAE could generate synthetic data with statistical properties similar to real data, TVAE outperformed CTGAN in terms of preserving the correlation structure of the data and the relationship between the features and the target variable, DMC. However, the synthetic data fell short in fooling machine learning classifiers, indicating a persisting challenge in synthetic data generation. With respect to utility, neither synthetic dataset produced by CTGAN or TVAE could serve as a satisfactory substitute for real data in training machine learning models for predicting DMC. Although TVAE-generated synthetic data showed some potential when used with Random For- est (RF) and K-Nearest Neighbors (KNN) classifiers, the performance was still inadequate for practical use. This study offers valuable insights into the use of generative models for synthetic NIR spectral data generation, highlighting their current limitations and potential areas for future research

Latent Diffusion Model for DNA Sequence Generation

The harnessing of machine learning, especially deep generative models, has opened up promising avenues in the field of synthetic DNA sequence generation. Whilst Generative Adversarial Networks (GANs) have gained traction for this application, they often face issues such as limited sample diversity and mode collapse. On the other hand, Diffusion Models are a promising new class of generative models that are not burdened with these problems, enabling them to reach the state-of-the-art in domains such as image generation. In light of this, we propose a novel latent diffusion model, DiscDiff, tailored for discrete DNA sequence generation. By simply embedding discrete DNA sequences into a continuous latent space using an autoencoder, we are able to leverage the powerful generative abilities of continuous diffusion models for the generation of discrete data. Additionally, we introduce Fr\'echet Reconstruction Distance (FReD) as a new metric to measure the sample quality of DNA sequence generations. Our DiscDiff model demonstrates an ability to generate synthetic DNA sequences that align closely with real DNA in terms of Motif Distribution, Latent Embedding Distribution (FReD), and Chromatin Profiles. Additionally, we contribute a comprehensive cross-species dataset of 150K unique promoter-gene sequences from 15 species, enriching resources for future generative modelling in genomics. We will make our code public upon publication