https:\/\/zoom.us\/j\/2445226856<\/a><\/p>\n\n\n\nMembres du jury :<\/strong><\/p>\n\n\n\nM. Noel CRESPI<\/strong>, Professeur, T\u00e9l\u00e9com SudParis, FRANCE – Directeur de these
Mme Praboda RAJAPAKSHA<\/strong>, Ma\u00eetre de conf\u00e9rences, Aberystwyth University, ROYAUME-UNI – Co-encadrant de these
M. Luis SANCHEZ<\/strong>, Associate Professor, University of Cantabria, ESPAGNE – Examinateur
M. GYU MYOUNG LEE<\/strong>, Professeur, Liverpool John Moores University,, ROYAUME-UNI – Examinateur
M. Abdelhamid MELLOUK<\/strong>, Professor, University of Paris-Est (UPEC), FRANCE – Rapporteur
M. Lei WANG<\/strong>, Professeur, Dalian University of Technology, CHINE – Rapporteur<\/p>\n\n\n\n\u00ab Clustering de donn\u00e9es multivues incompl\u00e8tes \u00e0 l’aide de techniques de mining de donn\u00e9es cach\u00e9es et de fusion \u00bb<\/h2>\n\n\n\npr\u00e9sent\u00e9 par Madame Zhenjiao LIU<\/h2>\n\n\n\n
R\u00e9sum\u00e9 :<\/strong><\/p>\n\n\n\nLe regroupement de donn\u00e9es multivues incompl\u00e8tes est un axe de recherche majeur dans le domaines de l’exploration de donn\u00e9es et de l’apprentissage automatique. Dans les applications pratiques, nous sommes souvent confront\u00e9s \u00e0 des situations o\u00f9 seule une partie des donn\u00e9es modales peut \u00eatre obtenue ou lorsqu’il y a des valeurs manquantes. La fusion de donn\u00e9es est une m\u00e9thode clef pour l’exploration d’informations multivues incompl\u00e8tes. R\u00e9soudre le probl\u00e8me de l’extraction d’informations multivues incompl\u00e8tes de mani\u00e8re cibl\u00e9e, parvenir \u00e0 une collaboration flexible entre les vues visibles et les vues cach\u00e9es partag\u00e9es, et am\u00e9liorer la robustesse sont des d\u00e9fis. Cette th\u00e8se se concentre sur trois aspects : l’exploration de donn\u00e9es cach\u00e9es, la fusion collaborative et l’am\u00e9lioration de la robustesse du regroupement. Les principales contributions sont les suivantes : 1) Exploration de donn\u00e9es cach\u00e9es pour les donn\u00e9es multi-vues incompl\u00e8tes : les algorithmes existants ne peuvent pas utiliser pleinement l’observation des informations dans et entre les vues, ce qui entra\u00eene la perte d’une grande quantit\u00e9 d’informations. Nous proposons donc un nouveau mod\u00e8le de regroupement multi-vues incomplet IMC-NLT (Incomplete Multi-view Clustering Based on NMF and Low-Rank Tensor Fusion) bas\u00e9 sur la factorisation de matrices non n\u00e9gatives et la fusion de tenseurs de faible rang. 2) Fusion collaborative pour les donn\u00e9es multivues incompl\u00e8tes : notre approche pour r\u00e9soudre ce probl\u00e8me est le regroupement multivues incomplet par repr\u00e9sentation \u00e0 faible rang. L’algorithme est bas\u00e9 sur une repr\u00e9sentation \u00e9parse de faible rang et une repr\u00e9sentation de sous-espace, dans laquelle les donn\u00e9es manquantes sont compl\u00e9t\u00e9es en utilisant les donn\u00e9es d’une modalit\u00e9 et les donn\u00e9es connexes d’autres modalit\u00e9s. 3) Am\u00e9lioration de la robustesse du regroupement pour les donn\u00e9es multivues incompl\u00e8tes : nous proposons une fusion de la convolution graphique et des goulots d’\u00e9tranglement de l’information (apprentissage de la repr\u00e9sentation multivues incompl\u00e8te via le goulot d’\u00e9tranglement de l’information). Nous introduisons la th\u00e9orie du goulot d’\u00e9tranglement de l’information afin de filtrer les donn\u00e9es parasites contenant des d\u00e9tails non pertinents et de ne conserver que les \u00e9l\u00e9ments les plus pertinents. Nous int\u00e9grons les informations sur la structure du graphe bas\u00e9es sur les points d’ancrage dans les informations sur le graphe local. Le mod\u00e8le int\u00e8gre des repr\u00e9sentations multiples \u00e0 l’aide de goulets d’\u00e9tranglement de l’information, r\u00e9duisant ainsi l’impact des informations redondantes dans les donn\u00e9es.<\/p>\n\n\n\n
Abstract :<\/strong><\/p>\n\n\n\nIncomplete multi-view data clustering is a research direction that attracts attention in the fields of data mining and machine learning. In practical applications, we often face situations where only part of the modal data can be obtained or there are missing values. Data fusion is an important method for incomplete multi-view information mining. Solving for incomplete multi-view information mining in a targeted manner, achieving flexible collaboration between visible views and shared hidden views, and improving the robustness have become quite challenging . This thesis focuses on three aspects: hidden data mining, collaborative fusion, and enhancing the robustness of clustering. The main contributions are as follows: 1. Hidden data mining for incomplete multi-view data: existing algorithms cannot make full use of the observation of information within and between views, resulting in the loss of a large amount of valuable information, and so we propose a new incomplete multi-view clustering model IMC-NLT (Incomplete Multi-view Clustering Based on NMF and Low-Rank Tensor Fusion) based on non-negative matrix factorization and low-rank tensor fusion. 2. Collaborative fusion for incomplete multi-view data: our approach to address this issue is Incomplete Multi-view Co-Clustering by Sparse Low-Rank Representation. The algorithm is based on sparse low-rank representation and subspace representation, in which jointly-missing data is filled using data within a modality and related data from other modalities. 3. Enhancing the clustering robustness for incomplete multi-view data: we offer a fusion of graph convolution and information bottlenecks (Incomplete Multi-view Representation Learning via Information Bottleneck and Anchor Graph GCN \u2013 IMRL-IG). First, we introduce the information bottleneck theory to filter out the noise data with irrelevant details and retain only the most relevant feature items. Next, we integrate the graph structure information based on anchor points into the local graph information of the state fused into the shared information representation and the information representation learning process of the local specific view, a process which can balance the robustness of the learned features and improve the robustness. Finally, the model integrates multiple representations with the help of information bottlenecks, reducing the impact of redundant information in the data.<\/p>\n","protected":false},"excerpt":{"rendered":"
L’Ecole doctorale : Ecole Doctorale de l’Institut Polytechnique de Paris et le Laboratoire de recherche SAMOVAR – Services r\u00e9partis, Architectures, Mod\u00e9lisation, Validation, Administration des R\u00e9seaux pr\u00e9sentent l\u2019AVIS DE SOUTENANCE de Madame Zhenjiao LIU Autoris\u00e9e \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Institut Polytechnique de Paris, pr\u00e9par\u00e9 \u00e0 T\u00e9l\u00e9com SudParis en : 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