68 research outputs found
visone - Software for the Analysis and Visualization of Social Networks
We present the software tool visone which combines graph-theoretic methods for the analysis of social networks with tailored means of visualization. Our main contribution is the design of novel graph-layout algorithms which accurately reflect computed analyses results in well-arranged drawings of the networks under consideration. Besides this, we give a detailed description of the design of the software tool and the provided analysis methods
Maximum a Posteriori Inference of Random Dot Product Graphs via Conic Programming
We present a convex cone program to infer the latent probability matrix of a
random dot product graph (RDPG). The optimization problem maximizes the
Bernoulli maximum likelihood function with an added nuclear norm regularization
term. The dual problem has a particularly nice form, related to the well-known
semidefinite program relaxation of the MaxCut problem. Using the primal-dual
optimality conditions, we bound the entries and rank of the primal and dual
solutions. Furthermore, we bound the optimal objective value and prove
asymptotic consistency of the probability estimates of a slightly modified
model under mild technical assumptions. Our experiments on synthetic RDPGs not
only recover natural clusters, but also reveal the underlying low-dimensional
geometry of the original data. We also demonstrate that the method recovers
latent structure in the Karate Club Graph and synthetic U.S. Senate vote graphs
and is scalable to graphs with up to a few hundred nodes.Comment: submitted for publication in SIAM Journal on Optimization (SIOPT
Evolution of A Common Vector Space Approach to Multi-Modal Problems
A set of methods to address computer vision problems has been developed. Video un- derstanding is an activate area of research in recent years. If one can accurately identify salient objects in a video sequence, these components can be used in information retrieval and scene analysis. This research started with the development of a course-to-fine frame- work to extract salient objects in video sequences. Previous work on image and video frame background modeling involved methods that ranged from simple and efficient to accurate but computationally complex. It will be shown in this research that the novel approach to implement object extraction is efficient and effective that outperforms the existing state-of-the-art methods. However, the drawback to this method is the inability to deal with non-rigid motion.
With the rapid development of artificial neural networks, deep learning approaches are explored as a solution to computer vision problems in general. Focusing on image and text, the image (or video frame) understanding can be achieved using CVS. With this concept, modality generation and other relevant applications such as automatic im- age description, text paraphrasing, can be explored. Specifically, video sequences can be modeled by Recurrent Neural Networks (RNN), the greater depth of the RNN leads to smaller error, but that makes the gradient in the network unstable during training.To overcome this problem, a Batch-Normalized Recurrent Highway Network (BNRHN) was developed and tested on the image captioning (image-to-text) task. In BNRHN, the highway layers are incorporated with batch normalization which diminish the gradient vanishing and exploding problem. In addition, a sentence to vector encoding framework that is suitable for advanced natural language processing is developed. This semantic text embedding makes use of the encoder-decoder model which is trained on sentence paraphrase pairs (text-to-text). With this scheme, the latent representation of the text is shown to encode sentences with common semantic information with similar vector rep- resentations. In addition to image-to-text and text-to-text, an image generation model is developed to generate image from text (text-to-image) or another image (image-to- image) based on the semantics of the content. The developed model, which refers to the Multi-Modal Vector Representation (MMVR), builds and encodes different modalities into a common vector space that achieve the goal of keeping semantics and conversion between text and image bidirectional. The concept of CVS is introduced in this research to deal with multi-modal conversion problems. In theory, this method works not only on text and image, but also can be generalized to other modalities, such as video and audio. The characteristics and performance are supported by both theoretical analysis and experimental results. Interestingly, the MMVR model is one of the many possible ways to build CVS. In the final stages of this research, a simple and straightforward framework to build CVS, which is considered as an alternative to the MMVR model, is presented
DCSI -- An improved measure of cluster separability based on separation and connectedness
Whether class labels in a given data set correspond to meaningful clusters is
crucial for the evaluation of clustering algorithms using real-world data sets.
This property can be quantified by separability measures. A review of the
existing literature shows that neither classification-based complexity measures
nor cluster validity indices (CVIs) adequately incorporate the central aspects
of separability for density-based clustering: between-class separation and
within-class connectedness. A newly developed measure (density cluster
separability index, DCSI) aims to quantify these two characteristics and can
also be used as a CVI. Extensive experiments on synthetic data indicate that
DCSI correlates strongly with the performance of DBSCAN measured via the
adjusted rand index (ARI) but lacks robustness when it comes to multi-class
data sets with overlapping classes that are ill-suited for density-based hard
clustering. Detailed evaluation on frequently used real-world data sets shows
that DCSI can correctly identify touching or overlapping classes that do not
form meaningful clusters
- …