Colorization aims at recovering the original color of a monochrome image from only a few color pixels. A state-of-the-art approach is based on matrix completion, which assumes that the target color image is low-rank. However, this low-rank assumption is often invalid on natural images. In this paper, we propose a patch-based approach that divides the image into patches and then imposes a low-rank structure only on groups of similar patches. Each local matrix completion problem is solved by an accelerated version of alternating direction method of multipliers (ADMM), and each AD-MM subproblem is solved efficiently by divide-and-conquer. Experiments on a number of benchmark images demonstrate that the proposed method outperforms existing approaches.

We examine the fundamental problem of background modeling which is to model the background scenes in video sequences and segment the moving objects from the background. A novel approach is proposed based on the Restricted Boltzmann Machine (RBM) while exploiting the temporal nature of the problem. In particular, we augment the standard RBM to take a window of sequential video frames as input and generate the background model while enforcing the background smoothly adapting to the temporal changes. As a result, the augmented temporally adaptive model can generate stable background given noisy inputs and adapt quickly to the changes in background while keeping all the advantages of RBMs including exact inference and effective learning procedure. Experimental results demonstrate the effectiveness of the proposed method in modeling the temporal nature in background.

In multi-task learning, multiple related tasks are considered simultaneously, with the goal to improve the generalization performance by utilizing the intrinsic sharing of information across tasks. This paper presents a multi-task learning approach by modeling the task-feature relationships. Specifically, instead of assuming that similar tasks have similar weights on all the features, we start with the motivation that the tasks should be related in terms of subsets of features, which implies a co-cluster structure. We design a novel regularization term to capture this task-feature co-cluster structure. A proximal algorithm is adopted to solve the optimization problem. Convincing experimental results demonstrate the effectiveness of the proposed algorithm and justify the idea of exploiting the task-feature relationships.

Human gait has been shown to be an efficient biometric measure for person identification at a distance. However, it often needs different gait features to handle various covariate conditions including viewing angles, walking speed, carrying an object and wearing different types of shoes. In order to improve the robustness of gait-based person re-identification on such multi-covariate conditions, a novel Swiss-system based cascade ranking model is proposed in this paper. Since the ranking model is able to learn a subspace where the potential true match is given the highest ranking, we formulate the gait-based person re-identification as a bipartite ranking problem and utilize it as an effective way for multi-feature ensemble learning. Then a Swiss multi-round competition system is developed for the cascade ranking model to optimize its effectiveness and efficiency. Extensive experiments on three indoor and outdoor public datasets demonstrate that our model outperforms several state-of-the-art methods remarkably.

With the pervasion of social media, topic identification in short texts attracts increasing attention in  recent years. However, in nature the texts of social media are short and noisy, and the structures are sparse and dynamic, resulting in difficulty to identify topic categories exactly from online social media. Inspired by social science findings that preference consistency and social contagion are observed in social media, we investigate topic identification in short and noisy texts by exploring social context from the perspective of social sciences. In particular, we present a mathematical optimization formulation that incorporates the preference consistency and social contagion theories into a supervised learning method, and conduct feature selection to tackle short and noisy texts in social media, which result in a Sociological framework for Topic Identification (STI). Experimental results on real-world datasets from Twitter and Citation Network demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of social context in topic identification.

In the real-world applications, heterogeneous interdependent attributes that consist of both discrete and numerical variables can be observed ubiquitously. The usual representation of these data sets is an information table, assuming the independence of attributes. However, very often, they are actually interdependent on one another, either explicitly or implicitly. Limited research has been conducted in analyzing such attribute interactions, which causes the analysis results to be more local than global. This paper proposes the coupled heterogeneous attribute analysis to capture the interdependence among mixed data by addressing coupling context and coupling weights in unsupervised learning. Such global couplings integrate the interactions within discrete attributes, within numerical attributes and across them to form the coupled representation for mixed type objects based on dimension conversion and feature selection. This work makes one step forward towards explicitly modeling the interdependence of heterogeneous attributes among mixed data, verified by the applications in data structure analysis, data clustering evaluation, and density comparison. Substantial experiments on 12 UCI data sets show that our approach can effectively capture the global couplings of heterogeneous attributes and outperforms the state-of-the-art methods, supported by statistical analysis.

Innovative methods have been developed for diagnosis, activity monitoring, and state estimation that achieve high accuracy through the use of stochastic models involving hybrid discrete and continuous behaviors. A key bottleneck is the automated acquisition of these hybrid models, and recent methods have focused predominantly on Jump Markov processes and piecewise autoregressive models. In this paper, we present a novel algorithm capable of performing unsupervised learning of guarded Probabilistic Hybrid Automata (PHA) models, which extends prior work by allowing stochastic discrete mode transitions in a hybrid system to have a functional dependence on its continuous state. Our experiments indicate that guarded PHA models can yield significant performance improvements when used by hybrid state estimators, particularly when diagnosing the true discrete mode of the system, without any noticeable impact on their real-time performance.

Learning to rank is an emerging learning task that opens up a diverse set of applications. However, most existing work focuses on learning a single ranking function whilst in many real world applications, there can be many ranking functions to fulfill various retrieval tasks on the same data set. How to train many ranking functions is challenging due to the limited availability of training data which is further compounded when plentiful training data is available for a small subset of the ranking functions. This is particularly true in settings, such as personalized ranking/retrieval, where each person requires a unique ranking function according to their preference, but only the functions of the persons who provide sufficient ratings (of objects, such as movies and music) can be well trained. To address this, we propose to construct a graph where each node corresponds to a retrieval task, and then propagate ranking functions on the graph. We illustrate the usefulness of the idea of propagating ranking functions and our method by exploring two real world applications.

Sampling repeated clinical laboratory tests with appropriate timing is challenging because the latent physiologic function being sampled is in general nonstationary. When ordering repeated tests, clinicians adopt various simple strategies that may or may not be well suited to the behavior of the function. Previous research on this topic has been primarily focused on cost-driven assessments of oversampling. But for monitoring physiologic state or for retrospective analysis, undersampling can be much more problematic than oversampling. In this paper we analyze hundreds of observation sequences of four different clinical laboratory tests to provide principled, data-driven estimates of undersampling and oversampling, and to assess whether the sampling adapts to changing volatility of the latent function. To do this, we developed a new method for fitting a Gaussian process to samples of a nonstationary latent function. Our method includes an explicit estimate of the latent function's volatility over time, which is deterministically related to its nonstationarity. We find on average that the degree of undersampling is up to an order of magnitude greater than oversampling, and that only a small minority are sampled with an adaptive strategy.

The majority of machine learning research has been focused on building models and inference techniques with sound mathematical properties and cutting edge performance. Little attention has been devoted to the development of data representation that can be used to improve a user's ability to interpret the data and machine learning models to solve real-world problems. In this paper, we quantitatively and qualitatively evaluate an efficient, accurate and scalable feature-compression method using latent Dirichlet allocation for discrete data. This representation can effectively communicate the characteristics of high-dimensional, complex data points. We show that the improvement of a user's interpretability through the use of a topic modeling-based compression technique is statistically significant, according to a number of metrics, when compared with other representations. Also, we find that this representation is scalable --- it maintains alignment with human classification accuracy as an increasing number of data points are shown. In addition, the learned topic layer can semantically deliver meaningful information to users that could potentially aid human reasoning about data characteristics in connection with compressed topic space.