Learning to rank has received great attention in recent years as it plays a crucial role in information retrieval. The existing concept of learning to rank assumes that each training sample is associated with an instance and a reliable label. However, in practice, this assumption does not necessarily hold true. This study focuses on the learning to rank when each training instance is labeled by multiple annotators that may be unreliable. In such a scenario, no accurate labels can be obtained. This study proposes two learning approaches. One is to simply estimate the ground truth first and then to learn a ranking model with it. The second approach is a maximum likelihood learning approach which estimates the ground truth and learns the ranking model iteratively. The two approaches have been tested on both synthetic and real-world data. The results reveal that the maximum likelihood approach outperforms the first approach significantly and is comparable of achieving results with the learning model considering reliable labels. Further more, both the approaches have been applied for ranking the Web visual clutter.

We consider the problem of learning to act in partially observable, continuous-state-and-action worlds where we have abstract prior knowledge about the structure of the optimal policy in the form of a distribution over policies. Using ideas from planning-as-inference reductions and Bayesian unsupervised learning, we cast Markov Chain Monte Carlo as a stochastic, hill-climbing policy search algorithm. Importantly, this algorithm's search bias is directly tied to the prior and its MCMC proposal kernels, which means we can draw on the full Bayesian toolbox to express the search bias, including nonparametric priors and structured, recursive processes like grammars over action sequences. Furthermore, we can reason about uncertainty in the search bias itself by constructing a hierarchical prior and reasoning about latent variables that determine the abstract structure of the policy. This yields an adaptive search algorithm---our algorithm learns to learn a structured policy efficiently. We show how inference over the latent variables in these policy priors enables intra- and intertask transfer of abstract knowledge. We demonstrate the flexibility of this approach by learning meta search biases, by constructing a nonparametric finite state controller to model memory, by discovering motor primitives using a simple grammar over primitive actions, and by combining all three.

Data sets containing multi-manifold structures are ubiquitous in real-world tasks, and effective grouping of such data is an important yet challenging problem. Though there were many studies on this problem, it is not clear on how to design principled methods for the grouping of multiple hybrid manifolds. In this paper, we show that spectral methods are potentially helpful for hybridmanifold clustering when the neighborhood graph is constructed to connect the neighboring samples from the same manifold. However, traditional algorithms which identify neighbors according to Euclidean distance will easily connect samples belonging to different manifolds. To handle this drawback, we propose a new criterion, i.e., local and structural consistency criterion, which considers the neighboring information as well as the structural information implied by the samples. Based on this criterion, we develop a simple yet effective algorithm, named Local and Structural Consistency (LSC), for clustering with multiple hybrid manifolds. Experiments show that LSC achieves promising performance.

NonnegativeMatrix Factorization (NMF) based coclustering methods have attracted increasing attention in recent years because of their mathematical elegance and encouraging empirical results. However, the algorithms to solve NMF problems usually involve intensive matrix multiplications, which make them computationally inefficient. In this paper, instead of constraining the factor matrices of NMF to be nonnegative as existing methods, we propose a novel Fast Nonnegative Matrix Trifactorization (FNMTF) approach to constrain them to be cluster indicator matrices, a special type of nonnegative matrices. As a result, the optimization problem of our approach can be decoupled, which results in much smaller size subproblems requiring much less matrix multiplications, such that our approach works well for large-scale input data. Moreover, the resulted factor matrices can directly assign cluster labels to data points and features due to the nature of indicator matrices. In addition, through exploiting the manifold structures in both data and feature spaces, we further introduce the Locality Preserved FNMTF (LP-FNMTF) approach, by which the clustering performance is improved. The promising results in extensive experimental evaluations validate the effectiveness of the proposed methods.

This paper describes a novel framework to jointly learn data-dependent label and locality-preserving projections. Given a set of data instances from multiple classes, the proposed approach can automatically learn which classes are more similar to each other, and construct discriminative features using both labeled and unlabeled data to map similar classes to similar locations in a lower dimensional space. In contrast to linear discriminant analysis (LDA) and its variants, which can only return c-1 features for a problem with c classes, the proposed approach can generate d features, where d is bounded only by the number of the input features. We describe and evaluate the new approach both theoretically and experimentally, and compare its performance with other state of the art methods.

We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications.

Text classification is widely used in many real-world applications. To obtain satisfied classification performance, most traditional data mining methods require lots of labeled data, which can be costly in terms of both time and human efforts. In reality, there are plenty of such resources in English since it has the largest population in the Internet world, which is not true in many other languages. In this paper, we present a novel transfer learning approach to tackle the cross-language text classification problems. We first align the feature spaces in both domains utilizing some on-line translation service, which makes the two feature spaces under the same coordinate. Although the feature sets in both domains are the same, the distributions of the instances in both domains are different, which violates the i.i.d. assumption in most traditional machine learning methods. For this issue, we propose an iterative feature and instance weighting (Bi-Weighting) method for domain adaptation. We empirically evaluate the effectiveness and efficiency of our approach. The experimental results show that our approach outperforms some baselines including four transfer learning algorithms.

The data at our disposal consists of onboard sensor measurements that have been collected from truck round-trips. We advocate the use of an explicit time representation By applying a simple discretization method, we obtain sequences in syntactic pattern recognition because it can of timed events. The behavior that is displayed in result in more succinct models and easier learning these sequences is unknown. From this data, we want to learn problems. We apply this approach to the real-world a model that we can use to monitor the driving behavior in problem of learning models for the driving behavior new data, i.e., to use it as a classifier. Our approach is to first of truck drivers. We discretize the values of learn a timed model from the unlabeled sequences using the onboard sensors into simple events.

We examine the utility of a curriculum (a means of presenting training samples in a meaningful order) in unsupervised learning of probabilistic grammars. We introduce the {\em incremental construction hypothesis} that explains the benefits of a curriculum in learning grammars and offers some useful insights into the design of curricula as well as learning algorithms. We present results of experiments with (a) carefully crafted synthetic data that provide support for our hypothesis and (b) natural language corpus that demonstrate the utility of curricula in unsupervised learning of probabilistic grammars.

Fraud detection is a key activity with serious socio-economical impact. Inspection activities associated with this task are usually constrained by limited available resources. Data analysis methods can provide help in the task of deciding where to allocate these limited resources in order to optimise the outcome of the inspection activities. This paper presents a multi-strategy learning method to address the question of which cases to inspect first. The proposed methodology is based on the utility theory and provides a ranking ordered by decreasing expected outcome of inspecting the candidate cases. This outcome is a function not only of the probability of the case being fraudulent but also of the inspection costs and expected payoff if the case is confirmed as a fraud. The proposed methodology is general and can be useful on fraud detection activities with limited inspection resources. We experimentally evaluate our proposal on both an artificial domain and on a real world task.