Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.

/pdf/a-survey-on-evolutionary-computation-approaches-to-feature-5c2qznm5vr.pdf

A Survey on Evolutionary Computation Approaches to Feature Selection

We propose an independence criterion based on the eigen-spectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

Measuring statistical dependence with Hilbert-Schmidt norms

Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

/pdf/digital-twin-values-challenges-and-enablers-from-a-modeling-3hpoelaogn.pdf

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Multimodal representation learning, which aims to narrow the heterogeneity gap among different modalities, plays an indispensable role in the utilization of ubiquitous multimodal data. Due to the powerful representation ability with multiple levels of abstraction, deep learning-based multimodal representation learning has attracted much attention in recent years. In this paper, we provided a comprehensive survey on deep multimodal representation learning which has never been concentrated entirely. To facilitate the discussion on how the heterogeneity gap is narrowed, according to the underlying structures in which different modalities are integrated, we category deep multimodal representation learning methods into three frameworks: joint representation, coordinated representation, and encoder-decoder. Additionally, we review some typical models in this area ranging from conventional models to newly developed technologies. This paper highlights on the key issues of newly developed technologies, such as encoder-decoder model, generative adversarial networks, and attention mechanism in a multimodal representation learning perspective, which, to the best of our knowledge, have never been reviewed previously, even though they have become the major focuses of much contemporary research. For each framework or model, we discuss its basic structure, learning objective, application scenes, key issues, advantages, and disadvantages, such that both novel and experienced researchers can benefit from this survey. Finally, we suggest some important directions for future work.

/pdf/deep-multimodal-representation-learning-a-survey-4g8th17lc7.pdf

Deep Multimodal Representation Learning: A Survey

Subspace learning for unsupervised feature selection via matrix factorization

A knowledge graph (KG), also known as a knowledge base, is a particular kind of network structure in which the node indicates entity and the edge represent relation. However, with the explosion of network volume, the problem of data sparsity that causes large-scale KG systems to calculate and manage difficultly has become more significant. For alleviating the issue, knowledge graph embedding is proposed to embed entities and relations in a KG to a low-, dense and continuous feature space, and endow the yield model with abilities of knowledge inference and fusion. In recent years, many researchers have poured much attention in this approach, and we will systematically introduce the existing state-of-the-art approaches and a variety of applications that benefit from these methods in this paper. In addition, we discuss future prospects for the development of techniques and application trends. Specifically, we first introduce the embedding models that only leverage the information of observed triplets in the KG. We illustrate the overall framework and specific idea and compare the advantages and disadvantages of such approaches. Next, we introduce the advanced models that utilize additional semantic information to improve the performance of the original methods. We divide the additional information into two categories, including textual descriptions and relation paths. The extension approaches in each category are described, following the same classification criteria as those defined for the triplet fact-based models. We then describe two experiments for comparing the performance of listed methods and mention some broader domain tasks such as question answering, recommender systems, and so forth. Finally, we collect several hurdles that need to be overcome and provide a few future research directions for knowledge graph embedding.

https://www.mdpi.com/2079-9292/9/5/750/pdf

A Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks

Rough set theory is a useful tool for dealing with the vagueness, granularity and uncertainty in information systems. This paper connects generalized rough sets based on relations with matroid theory. We define the upper approximation number to induce a matroid from a relation. Therefore, many matroidal approaches can be used to study generalized rough sets based on relations. Specifically, with the rank function of the matroid induced by a relation, we construct a pair of approximation operators, namely, matroid approximation operators. The matroid approximation operators present some unique properties which do not exist in the existing approximation operators. On the other hand, we present an approach to induce a relation from a matroid. Moreover, the relationship between two inductions is studied.

Matroidal approaches to generalized rough sets based on relations

High dimensionality is quite commonly encountered in data mining problems, and hence dimensionality reduction becomes an important task in order to improve the efficiency of learning algorithms. As a widely used technique of dimensionality reduction, feature selection is about selecting a feature subset being guided by certain criterion. In this paper, three unsupervised feature selection algorithms are proposed and addressed from the viewpoint of sparse graph embedding learning. First, using the self-characterization of the given data, we view the data themselves as a dictionary, conduct sparse coding and propose the sparsity preserving feature selection (SPFS) algorithm. Second, considering the locality preservation of neighborhoods for the data, we study a special case of the SPFS problem, namely, neighborhood preserving feature selection problem, and come up with a suitable algorithm. Third, we incorporate sparse coding and feature selection into one unified framework, and propose a neighborhood embedding feature selection (NEFS) criterion. Drawing support from nonnegative matrix factorization, the corresponding algorithm for NEFS is presented and its convergence is proved. Finally, the three proposed algorithms are validated with the use of eight publicly available real-world datasets from machine learning repository. Extensive experimental results demonstrate the superiority of the proposed algorithms over four compared state-of-the-art unsupervised feature selection methods.

Shiping Wang

Papers

Deep Multimodal Representation Learning: A Survey

Subspace learning for unsupervised feature selection via matrix factorization

A Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks

Matroidal approaches to generalized rough sets based on relations

Sparse Graph Embedding Unsupervised Feature Selection