A Survey of Matrix Completion Methods for Recommendation Systems

In recent years, the recommendation systems have become increasingly popular and have been used in a broad variety of applications. Here, we investigate the matrix completion techniques for the recommendation systems that are based on collaborative filtering. The collaborative filtering problem can be viewed as predicting the favorability of a user with respect to new items of commodities. When a rating matrix is constructed with users as rows, items as columns, and entries as ratings, the collaborative filtering problem can then be modeled as a matrix completion problem by filling out the unknown elements in the rating matrix. This article presents a comprehensive survey of the matrix completion methods used in recommendation systems. We focus on the mathematical models for matrix completion and the corresponding computational algorithms as well as their characteristics and potential issues. Several applications other than the traditional user-item association prediction are also discussed

Discovering a Domain Knowledge Representation for Image Grouping: Multimodal Data Modeling, Fusion, and Interactive Learning

In visually-oriented specialized medical domains such as dermatology and radiology, physicians explore interesting image cases from medical image repositories for comparative case studies to aid clinical diagnoses, educate medical trainees, and support medical research. However, general image classification and retrieval approaches fail in grouping medical images from the physicians\u27 viewpoint. This is because fully-automated learning techniques cannot yet bridge the gap between image features and domain-specific content for the absence of expert knowledge. Understanding how experts get information from medical images is therefore an important research topic. As a prior study, we conducted data elicitation experiments, where physicians were instructed to inspect each medical image towards a diagnosis while describing image content to a student seated nearby. Experts\u27 eye movements and their verbal descriptions of the image content were recorded to capture various aspects of expert image understanding. This dissertation aims at an intuitive approach to extracting expert knowledge, which is to find patterns in expert data elicited from image-based diagnoses. These patterns are useful to understand both the characteristics of the medical images and the experts\u27 cognitive reasoning processes. The transformation from the viewed raw image features to interpretation as domain-specific concepts requires experts\u27 domain knowledge and cognitive reasoning. This dissertation also approximates this transformation using a matrix factorization-based framework, which helps project multiple expert-derived data modalities to high-level abstractions. To combine additional expert interventions with computational processing capabilities, an interactive machine learning paradigm is developed to treat experts as an integral part of the learning process. Specifically, experts refine medical image groups presented by the learned model locally, to incrementally re-learn the model globally. This paradigm avoids the onerous expert annotations for model training, while aligning the learned model with experts\u27 sense-making

3D Robotic Sensing of People: Human Perception, Representation and Activity Recognition

The robots are coming. Their presence will eventually bridge the digital-physical divide and dramatically impact human life by taking over tasks where our current society has shortcomings (e.g., search and rescue, elderly care, and child education). Human-centered robotics (HCR) is a vision to address how robots can coexist with humans and help people live safer, simpler and more independent lives. As humans, we have a remarkable ability to perceive the world around us, perceive people, and interpret their behaviors. Endowing robots with these critical capabilities in highly dynamic human social environments is a significant but very challenging problem in practical human-centered robotics applications. This research focuses on robotic sensing of people, that is, how robots can perceive and represent humans and understand their behaviors, primarily through 3D robotic vision. In this dissertation, I begin with a broad perspective on human-centered robotics by discussing its real-world applications and significant challenges. Then, I will introduce a real-time perception system, based on the concept of Depth of Interest, to detect and track multiple individuals using a color-depth camera that is installed on moving robotic platforms. In addition, I will discuss human representation approaches, based on local spatio-temporal features, including new “CoDe4D” features that incorporate both color and depth information, a new “SOD” descriptor to efficiently quantize 3D visual features, and the novel AdHuC features, which are capable of representing the activities of multiple individuals. Several new algorithms to recognize human activities are also discussed, including the RG-PLSA model, which allows us to discover activity patterns without supervision, the MC-HCRF model, which can explicitly investigate certainty in latent temporal patterns, and the FuzzySR model, which is used to segment continuous data into events and probabilistically recognize human activities. Cognition models based on recognition results are also implemented for decision making that allow robotic systems to react to human activities. Finally, I will conclude with a discussion of future directions that will accelerate the upcoming technological revolution of human-centered robotics

Fast and modular regularized topic modelling

Topic modelling is an area of text mining that has been actively developed in the last 15 years. A probabilistic topic model extracts a set of hidden topics from a collection of text documents. It defines each topic by a probability distribution over words and describes each document with a probability distribution over topics. In applications, there are often many requirements, such as, for example, problem-specific knowledge and additional data, to be taken into account. Therefore, it is natural for topic modelling to be considered a multiobjective optimization problem. However, historically, Bayesian learning became the most popular approach for topic modelling. In the Bayesian paradigm, all requirements are formalized in terms of a probabilistic generative process. This approach is not always convenient due to some limitations and technical difficulties. In this work, we develop a non-Bayesian multiobjective approach called the Additive Regularization of Topic Models (ARTM). It is based on regularized Maximum Likelihood Estimation (MLE), and we show that many of the well-known Bayesian topic models can be re-formulated in a much simpler way using the regularization point of view. We review some of the most important types of topic models: multimodal, multilingual, temporal, hierarchical, graph-based, and short-text. The ARTM framework enables easy combination of different types of models to create new models with the desired properties for applications. This modular “lego-style” technology for topic modelling is implemented in the open-source library BigARTM

A Review of Codebook Models in Patch-Based Visual Object Recognition

The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods

Nonnegative approximations of nonnegative tensors

We study the decomposition of a nonnegative tensor into a minimal sum of outer product of nonnegative vectors and the associated parsimonious naive Bayes probabilistic model. We show that the corresponding approximation problem, which is central to nonnegative PARAFAC, will always have optimal solutions. The result holds for any choice of norms and, under a mild assumption, even Bregman divergences.Comment: 14 page