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Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Survey

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arXiv 2021年

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. We first explain how the spectral dimensionality reduction methods can be unified as kernel Principal Component Analysis (PCA) with different kernels. This unification can be interpreted as eigenfunction learning or representation of kernel in terms of distance matrix. Then, since the spectral methods are unified as kernel PCA, we say let us learn the best kernel for unfolding the manifold of data to its maximum variance. We first briefly introduce kernel learning by SDP for the transduction task. Then, we explain MVU in detail. Various versions of supervised MVU using nearest neighbors graph, by class-wise unfolding, by Fisher criterion, and by colored MVU are explained. We also explain out-of-sample extension of MVU using eigenfunctions and kernel mapping. Finally, we introduce other variants of MVU including action respecting embedding, relaxed MVU, and landmark MVU for big data. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey

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arXiv 2021年

This is a tutorial and survey paper for nonlinear dimensionality and feature extraction methods which are based on the Laplacian of graph of data. We first introduce adjacency matrix, definition of Laplacian matrix, and the interpretation of Laplacian. Then, we cover the cuts of graph and spectral clustering which applies clustering in a subspace of data. Different optimization variants of Laplacian eigenmap and its out-of-sample extension are explained. Thereafter, we introduce the locality preserving projection and its kernel variant as linear special cases of Laplacian eigenmap. Versions of graph embedding are then explained which are generalized versions of Laplacian eigenmap and locality preserving projection. Finally, diffusion map is introduced which is a method based on Laplacian of data and random walks on the data graph. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

Homophobia and transphobia detection for low-resourced languages in social media comments

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Natural Language Processing Journal

Natural Language Processing Journal 2023年 5卷

作者： Prasanna Kumar Kumaresan Rahul Ponnusamy Ruba Priyadharshini Paul Buitelaar Bharathi Raja Chakravarthi Insight SFI Research Centre for Data Analytics Data Science Institute University of Galway Ireland Department of Mathematics Gandhigram Rural Institute-Deemed to be University Tamil Nadu India School of Computer Science University of Galway Ireland

People are increasingly sharing and expressing their emotions using online social media platforms such as Twitter, Facebook, and YouTube. An abusive, hateful, threatening, and discriminatory act that makes discomfort targets gay, lesbian, transgender, or bisexual individuals is called Homophobia and Transphobia. Detecting these types of acts on social media is called Homophobia and Transphobia Detection. This task has recently gained interest among researchers. Identifying homophobic and transphobic content for under-resourced languages is a bit challenging task. There are no such resources for Malayalam and Hindi to categorize these types of content as far now. This paper presents a new high-quality dataset for detecting homophobia and transphobia in Malayalam and Hindi languages. Our dataset consists of 5,193 comments in Malayalam and 3,203 comments in Hindi. We also submitted the experiments performed with traditional machine learning and transformer-based deep learning models on the Malayalam, Hindi, English, Tamil, and Tamil-English datasets.

关键词： dataset creation Under-resourced language Machine learning Transformer models Cross-lingual transfer learning Homophobia and transphobia Performance analysis

A Short Note on Relevant Cuts

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arXiv 2024年

作者： Domschke, Nico Gatter, Thomas Golnik, Richard Stadler, Peter F. Bioinformatics Group Department of Computer Science Leipzig University Härtelstraße 16–18 LeipzigD-04107 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig School of Embedded Composite Artificial Intelligence Leipzig University Härtelstraße 16–18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Department of Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

The set of relevant cuts in a graph is the union of all minimum weight bases of the cut space. We show that a cut is relevant if and only if it is the a minimum weight cut between two distinct vertices. Moreover, we give a characterization in terms of Picard-Queyranne DAGs that can be used to accelerate the enumeration of the relevant cuts. © 2024, CC BY-NC-ND.

关键词： Trees (mathematics)

Large Language Models in Mental Health Care: a Scoping Review

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arXiv 2024年

作者： Hua, Yining Liu, Fenglin Yang, Kailai Li, Zehan Na, Hongbin Sheu, Yi-Han Zhou, Peilin Moran, Lauren V. Ananiadou, Sophia CliDon, David A. Beam, Andrew Torous, John Department of Epidemiology Harvard T.H. Chan School of Public Health MA United States Department of Psychiatry Beth Israel Deaconess Medical Center MA United States Institute of Biomedical Engineering Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computer Science The University of Manchester Manchester United Kingdom School of Biomedical Informatics University of Texas Health Science at Houston Houston United States Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Center for Precision Psychiatry Massachusetts General Hospital MA United States Department of Psychiatry Harvard Medical School MA United States Data Science and Analytics Thrust Hong Kong University of Science and Technology Guangzhou China Division of Psychotic Disorders McLean Hospital MA United States The Alan Turing Institute London United Kingdom

The integration of large language models (LLMs) in mental health care is an emerging field. There is a need to systematically review the application outcomes and delineate the advantages and limitations in clinical settings. This review aims to provide a comprehensive overview of the use of LLMs in mental health care, assessing their efficacy, challenges, and potential for future applications. A systematic search was conducted across multiple databases including PubMed, Web of science, Google Scholar, arXiv, medRxiv, and PsyArXiv in November 2023. All forms of original research, peer-reviewed or not, published or disseminated between October 1, 2019, and December 2, 2023, are included without language restrictions if they used LLMs developed after T5 and directly addressed research questions in mental health care settings. From an initial pool of 313 articles, 34 met the inclusion criteria based on their relevance to LLM application in mental health care and the robustness of reported outcomes. Diverse applications of LLMs in mental health care are identified, including diagnosis, therapy, patient engagement enhancement, etc. Key challenges include data availability and reliability, nuanced handling of mental states, and effective evaluation methods. Despite successes in accuracy and accessibility improvement, gaps in clinical applicability and ethical considerations were evident, pointing to the need for robust data, standardized evaluations, and interdisciplinary collaboration. LLMs hold substantial promise for enhancing mental health care. For their full potential to be realized, emphasis must be placed on developing robust datasets, development and evaluation frameworks, ethical guidelines, and interdisciplinary collaborations to address current limitations. © 2024, CC BY.

关键词： Electronic health record

Deep CNN: A machine learning approach for driver drowsiness detection based on eye state

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Revue d'Intelligence Artificielle 2019年第6期33卷 461-466页

作者： Reddy Chirra, Venkata Rami Uyyala, Srinivasulu Reddy Kishore Kolli, Venkata Krishna Department of Computer Applications National Institute of Technology Tiruchirappalli620015 India Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology Tiruchirappalli620015 India Department of Computer Science and Engineering VFSTR Guntur522213 India

Driver drowsiness is one of the reasons for large number of road accidents these days. With the advancement in computer Vision technologies, smart/intelligent cameras are developed to identify drowsiness in drivers, thereby alerting drivers which in turn reduce accidents when they are in fatigue. In this work, a new framework is proposed using deep learning to detect driver drowsiness based on Eye state while driving the vehicle. To detect the face and extract the eye region from the face images, Viola-Jones face detection algorithm is used in this work. Stacked deep convolution neural network is developed to extract features from dynamically identified key frames from camera sequences and used for learning phase. A SoftMax layer in CNN classifier is used to classify the driver as sleep or non-sleep. This system alerts driver with an alarm when the driver is in sleepy mood. The proposed work is evaluated on a collected dataset and shows better accuracy with 96.42% when compared with traditional CNN. The limitation of traditional CNN such as pose accuracy in regression is overcome with the proposed Staked Deep CNN. © 2019 Lavoisier. All rights reserved.

关键词： Face recognition

CONCENTRATION INEQUALITIES AND OPTIMAL NUMBER OF LAYERS FOR STOCHASTIC DEEP NEURAL NETWORKS

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arXiv 2022年

作者： Caprio, Michele Mukherjee, Sayan PRECISE Center Department of Computer and Information Science University of Pennsylvania 3330 Walnut Street PhiladelphiaPA19104 United States Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Humboldtstraße 25 Leipzig04105 Germany The Max Planck Institute for Mathematics in the Sciences Inselstraße 22 Leipzig04103 Germany Departments of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics Duke University DurhamNC27708 United States

We state concentration inequalities for the output of the hidden layers of a stochastic deep neural network (SDNN), as well as for the output of the whole SDNN. These results allow us to introduce an expected classifier (EC), and to give probabilistic upper bound for the classification error of the EC. We also state the optimal number of layers for the SDNN via an optimal stopping procedure. We apply our analysis to a stochastic version of a feedforward neural network with ReLU activation *** Codes Primary: 62M45, Secondary: 60G42 © 2022, CC BY.

关键词： Deep neural networks

Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems

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arXiv 2021年

作者： Usevich, Konstantin Dreesen, Philippe Ishteva, Mariya Université de Lorraine CNRS CRAN Nancy France STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium KU Leuven Department of Computer Science ADVISE-NUMA campus Geel Belgium

We consider the problem of identifying a parallel Wiener-Hammerstein structure from Volterra kernels. Methods based on Volterra kernels typically resort to coupled tensor decompositions of the kernels. However, in the case of parallel Wiener-Hammerstein systems, such methods require nontrivial constraints on the factors of the decompositions. In this paper, we propose an entirely different approach: by using special sampling (operating) points for the Jacobian of the nonlinear map from past inputs to the output, we can show that the Jacobian matrix becomes a linear projection of a tensor whose rank is equal to the number of branches. This representation allows us to solve the identification problem as a tensor recovery problem. Copyright © 2021, The Authors. All rights reserved.

关键词： Tensors

Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems

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IFAC-PapersOnLine 2021年第7期54卷 463-468页

作者： Konstantin Usevich Philippe Dreesen Mariya Ishteva Université de Lorraine CNRS CRAN Nancy France KU Leuven Dept. Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium KU Leuven Department of Computer Science ADVISE-NUMA campus Geel Belgium

关键词： Block structured system identification parallel Wiener-Hammerstein systems Volterra kernels low-rank tensor recovery canonical polyadic decomposition