Yasser KHALAFAOUI

Data is increasingly being collected from multiple sources and described by multiple views. These multi-view data provide richer information than traditional single-view data. Fusing the former for specific tasks is an essential component of multi-view clustering. Since the goal of multi-view clustering algorithms is to discover the common latent structure shared by multiple views, the majority of proposed solutions overlook the advantages of incorporating knowledge derived from horizontal collaboration between multi-view data and the final consensus. To fill this gap, we propose the Joint Multi-View Collaborative Clustering (JMVCC) solution, which involves the generation of basic partitions using Non-negative Matrix Factorization (NMF) and the horizontal collaboration principle, followed by the fusion of these local partitions using ensemble clustering. Furthermore, we propose a weighting method to reduce the risk of negative collaboration (i.e., views with low quality) during the generation and fusion of local partitions. The experimental results, which were obtained using a variety of data sets, demonstrate that JMVCC outperforms other multi-view clustering algorithms and is robust to noisy views.

By combining related objects, unsupervised machine learning techniques aim to reveal the underlying patterns in a data set. Non-negative Matrix Factorization (NMF) is a data mining technique that splits data matrices by imposing restrictions on the elements’ non-negativity into two matrices: one representing the data partitions and the other to represent the cluster prototypes of the data set. This method has attracted a lot of attention and is used in a wide range of applications, including text mining, clustering, language modeling, music transcription, and neuroscience (gene separation). The interpretation of the generated matrices is made simpler by the absence of negative values. In this article, we propose a study on multi-modal clustering algorithms and present a novel method called multi-modal multi-view non-negative matrix factorization, in which we analyze the collaboration of several local NMF models. The experimental results show the value of the proposed approach, which was evaluated using a variety of data sets, and the obtained results are very promising compared to state of art methods.

Chatbots allow computer programs to interact naturally with a user. However, they remain limited due to their lack of sensitivity to the user’s state of mind and emotions. This sensitivity will allow the chatbots to provide more accurate answers. Text-based emotion detection has already been explored for the english language (Chatterjee et al., 2019), yet no satisfying french dataset is available. We propose to translate the emotion corpus of multi-party conversation EmotionLines, which is based on the Friends TV show, by exploiting its french broadcasting. Our translation-based dataset generation method is adaptable to any dataset deriving from foreign movies, or TV shows broadcasted in french. Using this translated dataset, we propose a classifier based on BERT, able to detect the user’s emotion from text. It takes into account the context of the discussion to improve its inferences.