Topic modeling and word embedding are two important techniques for deriving latent semantics from data. General-purpose topic models typically work in coarse granularity by capturing word co-occurrence at the document/sentence level. In contrast, word embedding models usually work in much finer granularity by modeling word co-occurrence within small sliding windows. With the aim of deriving latent semantics by considering word co-occurrence at different levels of granularity, we propose a novel model named Latent Topic Embedding (LTE), which seamlessly integrates topic generation and embedding learning in one unified framework. We further propose an efficient Monte Carlo EM algorithm to estimate the parameters of interest. By retaining the individual advantages of topic modeling and word embedding, LTE results in better latent topics and word embedding. Extensive experiments verify the superiority of LTE over the state-of-the-arts.

The sparse additive model for text modeling involves the sum-of-exp computing, with consuming costs for large scales. Moreover, the assumption of equal background across all classes/topics may be too strong. This paper extends to propose sparse additive model with low rank background (SAM-LRB), and simple yet efficient estimation. Particularly, by employing a double majorization bound, we approximate the log-likelihood into a quadratic lower-bound with the sum-of-exp terms absent. The constraints of low rank and sparsity are then simply embodied by nuclear norm and $\ell_1$-norm regularizers. Interestingly, we find that the optimization task in this manner can be transformed into the same form as that in Robust PCA. Consequently, parameters of supervised SAM-LRB can be efficiently learned using an existing algorithm for Robust PCA based on accelerated proximal gradient. Besides the supervised case, we extend SAM-LRB to also favor unsupervised and multifaceted scenarios. Experiments on real world data demonstrate the effectiveness and efficiency of SAM-LRB, showing state-of-the-art performances.

Improving recommendation accuracy is the mostly focused target of recommendation systems, while it has been increasingly recognized that accuracy is not enough as the only quality criterion. More concepts have been proposed recently to augment the evaluation dimensions, such as similarity, diversity, long-tail, etc. Simultaneously considering multiple criteria leads to a multi-task recommendation. In this paper, a graph-based recommendation approach is proposed to effectively and flexibly trade-off among them.

For modeling data matrices, this paper introduces Probabilistic Co-Subspace Addition (PCSA) model by simultaneously capturing the dependent structures among both rows and columns. Briefly, PCSA assumes that each entry of a matrix is generated by the additive combination of the linear mappings of two features, which distribute in the row-wise and column-wise latent subspaces. Consequently, it captures the dependencies among entries intricately, and is able to model the non-Gaussian and heteroscedastic density. Variational inference is proposed on PCSA for approximate Bayesian learning, where the updating for posteriors is formulated into the problem of solving Sylvester equations. Furthermore, PCSA is extended to tackling and filling missing values, to adapting its sparseness, and to modelling tensor data. In comparison with several state-of-art approaches, experiments demonstrate the effectiveness and efficiency of Bayesian (sparse) PCSA on modeling matrix (tensor) data and filling missing values.

We propose a clustering algorithm based on a structural prior based Local Factor Analysis (spLFA) model under the Bayesian Ying-Yang harmony learning, which automatically determines the hidden dimensionalities during parameter learning, reduces the number of free parameters by projecting the mean vectors onto a low dimensional manifold, imposes the sparseness by a Normal-Jeffreys prior. Experiments on the diagnostic research dataset show that BYY-spLFA outperforms the k-means clustering and single-link hierarchical clustering. The experiments on a lymphoma cancer datset further indicate the BYY-spLFA is able to uncover the number of phenotypes correctly and cluster the phenotypes more accurately. In addition, we modify BYY-spLFA to implement supervised learning and preliminarily demonstrate its effectiveness on a Leukemia data for classification.

Radar high-resolution range profiles (HRRPs) are typical high-dimensional non-Gaussian and inter-dimensional dependently distributed data, the statistical modelling of which is a challenging task for HRRP based target recognition. Considering the inter-dimensional dependence, a recent work applied Factor Analysis (FA) to model radar HRRP data and showed promising recognition results, which however still restricts to Gaussian distribution. This paper aims to simultaneously consider the inter-dimensional dependence and the non-Gaussian distribution, by using Local Factor Analysis (LFA) model. For not only learning parameters but also appropriately selecting the component number and local hidden dimensionalities, we adopt the automatic Bayesian Ying-Yang (BYY) harmony learning, in order to relieve the extensive computation and inaccurate evaluation encountered in the conventional two-phase implementation. Moreover, a heuristic aspect-frame partition is implemented based on the BYY harmony criterion rather than AIC or BIC in the previous work, to tackle the radar HRRP's target-aspect sensitivity. Experiments show improved recognition performances over on the same measured HRRP dataset, i.e., for both equal interval and heuristic aspect-frame partitions, LFA automatically learned by BYY always outperforms FA selected by a two-phase procedure with either AIC or BIC.