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Found 11 papers, 1 papers with code
Generalised Mutual Information: a Framework for Discriminative Clustering
In the last decade, recent successes in deep clustering majorly involved the Mutual Information (MI) as an unsupervised objective for training neural networks with increasing regularisations.
The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges
To understand those heterogeneous and complex data structures, clustering nodes into homogeneous groups as well as rendering a comprehensible visualisation of the data is mandatory.
Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism
Semi-supervised learning is a powerful technique for leveraging unlabeled data to improve machine learning models, but it can be affected by the presence of ``informative'' labels, which occur when some classes are more likely to be labeled than others.
Sparse GEMINI for Joint Discriminative Clustering and Feature Selection
Feature selection in clustering is a hard task which involves simultaneously the discovery of relevant clusters as well as relevant variables with respect to these clusters.
Generalised Mutual Information for Discriminative Clustering
In the last decade, recent successes in deep clustering majorly involved the mutual information (MI) as an unsupervised objective for training neural networks with increasing regularisations.
Active Speaker Detection as a Multi-Objective Optimization with Uncertainty-based Multimodal Fusion
It is now well established from a variety of studies that there is a significant benefit from combining video and audio data in detecting active speakers.
Unobserved classes and extra variables in high-dimensional discriminant analysis
In supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase.
DeepLTRS: A Deep Latent Recommender System based on User Ratings and Reviews
The underlying motivation is that, when a user scores only a few products, the texts used in the reviews represent a significant source of information.
A Bayesian Fisher-EM algorithm for discriminative Gaussian subspace clustering
High-dimensional data clustering has become and remains a challenging task for modern statistics and machine learning, with a wide range of applications.
Image Denoising
Methodology
Exact Dimensionality Selection for Bayesian PCA
We present a Bayesian model selection approach to estimate the intrinsic dimensionality of a high-dimensional dataset.
Bayesian Variable Selection for Globally Sparse Probabilistic PCA
To this end, using Roweis' probabilistic interpretation of PCA and a Gaussian prior on the loading matrix, we provide the first exact computation of the marginal likelihood of a Bayesian PCA model.
