Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering

Semi-supervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled. The true number of clusters in the data is often unknown and most models require this parameter as an input. Dirichlet process mixture models are appealing as they can infer the number of clusters from the data. However, these models do not deal with high dimensional data well and can encounter difficulties in inference. We present a novel nonparameteric Bayesian kernel based method to cluster data points without the need to prespecify the number of clusters or to model complicated densities from which data points are assumed to be generated from. The key insight is to use determinants of submatrices of a kernel matrix as a measure of how close together a set of points are. We explore some theoretical properties of the model and derive a natural Gibbs based algorithm with MCMC hyperparameter learning. The model is implemented on a variety of synthetic and real world data sets.

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models

We propose a probabilistic model to infer supervised latent variables in the Hamming space from observed data. Our model allows simultaneous inference of the number of binary latent variables, and their values. The latent variables preserve neighbourhood structure of the data in a sense that objects in the same semantic concept have similar latent values, and objects in different concepts have dissimilar latent values. We formulate the supervised infinite latent variable problem based on an intuitive principle of pulling objects together if they are of the same type, and pushing them apart if they are not. We then combine this principle with a flexible Indian Buffet Process prior on the latent variables. We show that the inferred supervised latent variables can be directly used to perform a nearest neighbour search for the purpose of retrieval. We introduce a new application of dynamically extending hash codes, and show how to effectively couple the structure of the hash codes with continuously growing structure of the neighbourhood preserving infinite latent feature space.

Zebrafish eaf1 and eaf2/u19 Mediate Effective Convergence and Extension Movements through the Maintenance of wnt11 and wnt5 Expression

Studies have attributed several functions to the Eaf family, including tumor suppression and eye development. Given the potential association between cancer and development, we set forth to explore Eaf1 and Eaf2/U19 activity in vertebrate embryogenesis, using zebrafish. In situ hybridization revealed similar eaf1 and eaf2/u19 expression patterns. Morpholino-mediated knockdown of either eaf1 or eaf2/u19 expression produced similar morphological changes that could be reversed by ectopic expression of target or reciprocal-target mRNA. However, combination of Eaf1 and Eaf2/U19 (Eafs)-morpholinos increased the severity of defects, suggesting that Eaf1 and Eaf2/U19 only share some functional redundancy. The Eafs knockdown phenotype resembled that of embryos with defects in convergence and extension movements. Indeed, knockdown caused expression pattern changes for convergence and extension movement markers, whereas cell tracing experiments using kaeda mRNA showed a correlation between Eafs knockdown and cell migration defects. Cardiac and pancreatic differentiation markers revealed that Eafs knockdown also disrupted midline convergence of heart and pancreatic organ precursors. Noncanonical Wnt signaling plays a key role in both convergence and extension movements and midline convergence of organ precursors. We found that Eaf1 and Eaf2/U19 maintained expression levels of wnt11 and wnt5. Moreover, wnt11 or wnt5 mRNA partially rescued the convergence and extension movement defects occurring in eafs morphants. Wnt11 and Wnt5 converge on rhoA, so not surprisingly, rhoA mRNA more effectively rescued defects than either wnt11 or wnt5 mRNA alone. However, the ectopic expression of wnt11 and wnt5 did not affect eaf1 and eaf2/u19 expression. These data indicate that eaf1 and eaf2/u19 act upstream of noncanonical Wnt signaling to mediate convergence and extension movements.

Semantic transform: Weakly supervised semantic inference for relating visual attributes

Relative (comparative) attributes are promising for thematic ranking of visual entities, which also aids in recognition tasks. However, attribute rank learning often requires a substantial amount of relational supervision, which is highly tedious, and apparently impractical for real-world applications. In this paper, we introduce the Semantic Transform, which under minimal supervision, adaptively finds a semantic feature space along with a class ordering that is related in the best possible way. Such a semantic space is found for every attribute category. To relate the classes under weak supervision, the class ordering needs to be refined according to a cost function in an iterative procedure. This problem is ideally NP-hard, and we thus propose a constrained search tree formulation for the same. Driven by the adaptive semantic feature space representation, our model achieves the best results to date for all of the tasks of relative, absolute and zero-shot classification on two popular datasets. © 2013 IEEE.

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Natl Tech Univ Ukraine, Huazhong Univ Sci & Technol, Huazhong Normal Univ, Wuhan Univ, Ternopil Natl Econ Univ, IEEE Ukraine Sect, I&M CI Joint Chapter

Partially Supervised Neighbor Embedding for Example-Based Image Super-Resolution

Neighbor embedding algorithm has been widely used in example-based super-resolution reconstruction from a single frame, which makes the assumption that neighbor patches embedded are contained in a single manifold. However, it is not always true for complicated texture structure. In this paper, we believe that textures may be contained in multiple manifolds, corresponding to classes. Under this assumption, we present a novel example-based image super-resolution reconstruction algorithm with clustering and supervised neighbor embedding (CSNE). First, a class predictor for low-resolution (LR) patches is learnt by an unsupervised Gaussian mixture model. Then by utilizing class label information of each patch, a supervised neighbor embedding is used to estimate high-resolution (HR) patches corresponding to LR patches. The experimental results show that the proposed method can achieve a better recovery of LR comparing with other simple schemes using neighbor embedding.