Sequential modelling of high-dimensional data is an important problem that appears in many domains including model-based reinforcement learning and dynamics identification for control. Latent variable models applied to sequential (i.e., latent models) have been shown be a particularly effective probabilistic approach solve this problem, especially when dealing with images. However, application ...

In latent semantic analysis (LSA), we aim at modelling a large corpus of high-dimensional discrete data from probabilistic perspective. The Assumption: one data point can be modelled by latent factors, which account for the co-occurrence of items within the data. We are also interested in the clustering structure of the data, which may benefit from the latent factors of the items. For example: ...

In this paper, we study latent factor models with dependency structure in the latent space. We propose a general learning framework which induces sparsity on the undirected graphical model imposed on the vector of latent factors. A novel latent factor model SLFA is then proposed as a matrix factorization problem with a special regularization term that encourages collaborative reconstruction. Th...

Automatically organizing collections of images presents serious challenges to the current state-of-the art methods in image data mining. Often, what is required is that images taken in the same place, of the same thing, or of the same person be conceptually grouped together. To achieve this, we introduce the Geometric Latent Dirichlet Allocation (gLDA) model for unsupervised particular object d...

A two-step process is commonly used to evaluate data–model fit of latent variable path models, the first step addressing the measurement portion of the model and the second addressing the structural portion of the model. Unfortunately, even if the fit of the measurement portion of the model is perfect, the ability to assess the fit within the structural portion is affected by the quality of the...

Regular medical records are useful for medical practitioners to analyze and monitor patient’s health status especially for those with chronic disease, but such records are usually incomplete due to unpunctuality and absence of patients. In order to resolve the missing data problem over time, tensorbased model is suggested for missing data imputation in recent papers because this approach makes ...

Hierarchical latent class(HLC) models are tree-structured Bayesian networks where leaf nodes are observed while internal nodes are hidden. We explore the following two-stage approach for learning HLC models: One first identifies the shallow latent variables – latent variables adjacent to observed variables – and then determines the structure among the shallow and possibly some other “deep” late...

Latent Variable Models (LVMs) are a family of machine learning (ML) models that have been widely used in text mining, computer vision, computational biology, recommender system, to name a few. One central task in machine learning is to extract the latent knowledge and structure from observed data and LVMs elegantly fit into this task. LVMs consist of observed variables used for modeling observe...

In this paper, a faster method for embedding cryptographic information in the image ispresented by expressing the concept of latent prints (Steganography). Data is encrypted bytwo methods before embedding to increase reliability. Then they are embedded into the imageby a button, a method has been expressed to reduce potential noise impact on the wayinformation is encoded.