Bongard Problems: A Topological Data Analysis Approach

Learning Methods ◽

Unique Rule ◽

Thinking Processes ◽

The Right ◽

Minimal Information

Bongard problems are a set of 100 visual puzzles posed by M. M. Bongard, where each puzzle consists of twelve images separated into two groups of six images. The task is to find the unique rule separating the two classes in each given problem. The problems were first posed as a challenge for the AI community to test machines ability to imitate complex, context-depending thinking processes using only minimal information. Although some work was done to solve these problems, none of the previous approaches could automatically solve all of them. The present paper is a contribution to attack these problems with a different approach, combining the tools of persistent homology alongside with machine learning methods. In this work, we present an algorithm and show that it is able to solve problems involving differences in connectivity and size as examples, we also show that it can solve problems involving a much larger set of differences provided the right G-equivariant operators

Classification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps

Scientific Reports ◽

10.1038/s41598-021-90070-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Scott Broderick ◽

Ruhil Dongol ◽

Tianmu Zhang ◽

Krishna Rajan

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Crystal Chemistry ◽

Persistent Homology ◽

Hierarchical Classification ◽

Learning Tool ◽

Coordination Polyhedra ◽

Machine Learning Tool ◽

Topological Data

AbstractThis paper introduces the use of topological data analysis (TDA) as an unsupervised machine learning tool to uncover classification criteria in complex inorganic crystal chemistries. Using the apatite chemistry as a template, we track through the use of persistent homology the topological connectivity of input crystal chemistry descriptors on defining similarity between different stoichiometries of apatites. It is shown that TDA automatically identifies a hierarchical classification scheme within apatites based on the commonality of the number of discrete coordination polyhedra that constitute the structural building units common among the compounds. This information is presented in the form of a visualization scheme of a barcode of homology classifications, where the persistence of similarity between compounds is tracked. Unlike traditional perspectives of structure maps, this new “Materials Barcode” schema serves as an automated exploratory machine learning tool that can uncover structural associations from crystal chemistry databases, as well as to achieve a more nuanced insight into what defines similarity among homologous compounds.

Clinical Data Analysis: An Opportunity to Compare Machine Learning Methods

Procedia Computer Science ◽

10.1016/j.procs.2016.09.218 ◽

2016 ◽

Vol 100 ◽

pp. 731-738 ◽

Cited By ~ 6

Author(s):

A. Salcedo-Bernal ◽

M.P. Villamil-Giraldo ◽

A.D. Moreno-Barbosa

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Clinical Data ◽

Learning Methods ◽

Clinical Data Analysis

Possible Solutions to the Problems of Microfinance Organizations with the Application of Intelligent Methods of Machine Learning

The world of new economy ◽

10.26794/2220-6469-2018-12-2-66-71 ◽

2018 ◽

Vol 12 (2) ◽

pp. 66-71

Author(s):

A. V. Zolotaryuk ◽

I. A. Chechneva

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Subject Area ◽

Microfinance Institutions ◽

Learning Methods ◽

Economic Information ◽

The Subject ◽

Intelligent Methods

The authors consider the problems associated with the activities of microfinance organizations, and directions to eliminate them. The subject of the study is the need to introduce machine learning to solve urgent problems. Machine learning methods are increasingly being implemented to analyze financial and economic information, which reduces and eliminates some of the difficulties. Although currently these methods are not widely used in the field of microfinance institutions (MFIs), there are opportunities for their application. The aim of the work is to determine the prospects for the use of these methods in MFOs. The article describes the subject area of research, associated with MFIs. The authors identify the main groups of problems related to MFOs, consider the possibility of introducing machine learning for data analysis in this area and determine the main directions of the possible use of machine learning for MFIs. The authors concluded that such methods are applicable for assessing the performance of MFIs.

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

Persistence Bag-of-Words for Topological Data Analysis

10.24963/ijcai.2019/624 ◽

2019 ◽

Cited By ~ 1

Author(s):

Bartosz Zieliński ◽

Michał Lipiński ◽

Mateusz Juda ◽

Matthias Zeppelzauer ◽

Paweł Dłotko

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Mathematical Theory ◽

State Of The Art ◽

Persistent Homology ◽

Complex Structure ◽

Bag Of Words ◽

Seamless Integration ◽

Alternative Approaches

Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs). PDs exhibit, however, complex structure and are difficult to integrate in today's machine learning workflows. This paper introduces persistence bag-of-words: a novel and stable vectorized representation of PDs that enables the seamless integration with machine learning. Comprehensive experiments show that the new representation achieves state-of-the-art performance and beyond in much less time than alternative approaches.

Classifying Non-Sentential Utterances in Dialogue: A Machine Learning Approach

Computational Linguistics ◽

10.1162/coli.2007.33.3.397 ◽

2007 ◽

Vol 33 (3) ◽

pp. 397-427 ◽

Cited By ~ 17

Author(s):

Raquel Fernández ◽

Jonathan Ginzburg ◽

Shalom Lappin

Keyword(s):

Machine Learning ◽

Pilot Study ◽

Full Range ◽

Learning Approach ◽

Learning Methods ◽

Fine Grained ◽

Machine Learning Approach ◽

The Right

In this article we use well-known machine learning methods to tackle a novel task, namely the classification of non-sentential utterances (NSUs) in dialogue. We introduce a fine-grained taxonomy of NSU classes based on corpus work, and then report on the results of several machine learning experiments. First, we present a pilot study focused on one of the NSU classes in the taxonomy—bare wh-phrases or “sluices”—and explore the task of disambiguating between the different readings that sluices can convey. We then extend the approach to classify the full range of NSU classes, obtaining results of around an 87% weighted F-score. Thus our experiments show that, for the taxonomy adopted, the task of identifying the right NSU class can be successfully learned, and hence provide a very encouraging basis for the more general enterprise of fully processing NSUs.

Enhanced Defect Detection in Carbon Fiber Reinforced Polymer Composites via Generative Kernel Principal Component Thermography

Polymers ◽

10.3390/polym13050825 ◽

2021 ◽

Vol 13 (5) ◽

pp. 825

Author(s):

Kaixin Liu ◽

Zhengyang Ma ◽

Yi Liu ◽

Jianguo Yang ◽

Yuan Yao

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Carbon Fiber ◽

Defect Detection ◽

Principal Component ◽

Fiber Reinforced Polymer ◽

Carbon Fiber Reinforced Polymer ◽

Learning Methods ◽

Reinforced Polymer

Increasing machine learning methods are being applied to infrared non-destructive assessment for internal defects assessment of composite materials. However, most of them extract only linear features, which is not in accord with the nonlinear characteristics of infrared data. Moreover, limited infrared images tend to restrict the data analysis capabilities of machine learning methods. In this work, a novel generative kernel principal component thermography (GKPCT) method is proposed for defect detection of carbon fiber reinforced polymer (CFRP) composites. Specifically, the spectral normalization generative adversarial network is proposed to augment the thermograms for model construction. Sequentially, the KPCT method is used by feature mapping of all thermogram data using kernel principal component analysis, which allows for differentiation of defects and background in the dimensionality-reduced data. Additionally, a defect-background separation metric is designed to help the performance evaluation of data analysis methods. Experimental results on CFRP demonstrate the feasibility and advantages of the proposed GKPCT method.

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

Metabolites ◽

10.3390/metabo10060243 ◽

2020 ◽

Vol 10 (6) ◽

pp. 243 ◽

Cited By ~ 7

Author(s):

Ulf W. Liebal ◽

An N. T. Phan ◽

Malvika Sudhakar ◽

Karthik Raman ◽

Lars M. Blank

Keyword(s):

Machine Learning ◽

Mass Spectrometry ◽

Data Analysis ◽

Metabolic Engineering ◽

Data Representation ◽

Heterogeneous Data ◽

Supervised Machine Learning ◽

Support Vector ◽

Learning Methods ◽

Machine Learning Methods

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.

A Survey of Topological Machine Learning Methods

Frontiers in Artificial Intelligence ◽

10.3389/frai.2021.681108 ◽

2021 ◽

Vol 4 ◽

Author(s):

Felix Hensel ◽

Michael Moor ◽

Bastian Rieck

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Deep Neural Networks ◽

State Of The Art ◽

Personalised Medicine ◽

Machine Learning Algorithms ◽

Pure Mathematics ◽

Future Challenges

The last decade saw an enormous boost in the field of computational topology: methods and concepts from algebraic and differential topology, formerly confined to the realm of pure mathematics, have demonstrated their utility in numerous areas such as computational biology personalised medicine, and time-dependent data analysis, to name a few. The newly-emerging domain comprising topology-based techniques is often referred to as topological data analysis (TDA). Next to their applications in the aforementioned areas, TDA methods have also proven to be effective in supporting, enhancing, and augmenting both classical machine learning and deep learning models. In this paper, we review the state of the art of a nascent field we refer to as “topological machine learning,” i.e., the successful symbiosis of topology-based methods and machine learning algorithms, such as deep neural networks. We identify common threads, current applications, and future challenges.

Machine learning methods for analyzing genomic and phenomic data in psychiatry

V M BEKHTEREV REVIEW OF PSYCHIATRY AND MEDICAL PSYCHOLOGY ◽

10.31363/2313-7053-2019-4-1-127 ◽

2019 ◽

pp. 127

Author(s):

T. I. Nurgaliev

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Learning Methods ◽

Machine Learning Methods

This review briefly describes modern approaches of data analysis in psychiatry using machine learning and gives possible prospects and common obstacles of this approach.