In this paper we investigate the use of coactive learning in a multitask setting. In coactive learning, an expert presents the learner with a problem and the learner returns a candidate solution. The expert then improves on the solution if necessary and presents the improved solution to the learner. The goal for the learner is to learn to produce solutions which cannot be further improved by the expert while minimizing the average expert effort. In this paper, we consider the setting where there are multiple experts (tasks), and in each iteration one expert presents a problem to the learner. While the experts are expected to have different solution preferences, they are also assumed to share similarities, which should enable generalization across experts. We analyze several algorithms for this setting and derive bounds on the average expert effort during learning. Our main contribution is the balanced Perceptron algorithm, which is the first coactive learning algorithm that is both able to generalize across experts when possible, while also guaranteeing convergence to optimal solutions for individual experts. Our experiments in three domains confirm that this algorithm is effective in the multitask setting, compared to natural baselines.

This paper studies an interesting problem: is it possible to predict the crowd opinion about a movie before the movie is actually released? The crowd opinion is here expressed by the distribution of ratings given by a sufficient amount of people. Consequently, the pre-release crowd opinion prediction can be regarded as a Label Distribution Learning (LDL) problem. In order to solve this problem, a Label Distribution Support Vector Regressor (LDSVR) is proposed in this paper. The basic idea of LDSVR is to fit a sigmoid function to each component of the label distribution simultaneously by a multi-output support vector machine. Experimental results show that LDSVR can accurately predict peoples’s rating distribution about a movie just based on the pre-release metadata of the movie.

Random feature mappings have been successfully used for approximating non-linear kernels to scaleup kernel methods. Some work aims at speeding up the feature mappings, but brings increasing variance of the approximation. In this paper, we propose a novel random feature mapping method that uses a signed Circulant Random Matrix (CRM) instead of an unstructured random matrix to project input data. The signed CRM has linear space complexity as the whole signed CRM can be recovered from one column of the CRM, and ensures loglin-ear time complexity to compute the feature mapping using the Fast Fourier Transform (FFT). Theoretically, we prove that approximating Gaussian kernel using our mapping method is unbiased and does not increase the variance. Experimentally, we demonstrate that our proposed mapping method is time and space efficient while retaining similar accuracies with state-of-the-art random feature mapping methods. Our proposed random feature mapping method can be implemented easily and make kernel methods scalable and practical for large scale training and predicting problems.

The k-means algorithm is one of the most often used method for data clustering. However, the standard k-means can only be applied in the original feature space. The kernel k-means, which extends k-means into the kernel space, can be used to capture the non-linear structure and identify arbitrarily shaped clusters. Since both the standard k-means and kernel k-means apply the squared error to measure the distances between data points and cluster centers, a few outliers will cause large errors and dominate the objection function. Besides, the performance of kernel method is largely determined by the choice of kernel. Unfortunately, the most suitable kernel for a particular task is often unknown in advance. In this paper, we first present a robust k-means using l 2,1 -norm in the feature space and then extend it to the kernel space. To recap the powerfulness of kernel methods, we further propose a novel robust multiple kernel k-means (RMKKM) algorithm that simultaneously finds the best clustering label, the cluster membership and the optimal combination of multiple kernels. An alternating iterative schema is developed to find the optimal value. Extensive experiments well demonstrate the effectiveness of the proposed algorithms.

Solving multi-manifolds clustering problems that include delineating and resolving multiple intersections is a very challenging problem. In this paper we propose a novel procedure for clustering intersecting multi-manifolds and delineating junctions in high dimensional spaces. We propose to explicitly and directly resolve ambiguities near the intersections by using 2 properties: One is the position of the data points in the vicinity of the detected intersection; the other is the reliable estimation of the tangent spaces away from the intersections. We experiment with our method on a wide range of geometrically complex settings of convoluted intersecting manifolds, on which we demon- strate higher clustering performance than the state of the art. This includes tackling challenging geometric structures such as when the tangent spaces at the intersections points are not orthogonal.

In practical applications, it is often observed that high-dimensional features can yield good performance, while being more costly in both computation and storage. In this paper, we propose a novel method called Bayesian Hashing to learn an optimal Hamming embedding of high-dimensional features, with a focus on the challenging application of face recognition. In particular, a boosted random FERNs classification model is designed to perform efficient face recognition, in which bit correlations are elaborately approximated with a random permutation technique. Without incurring additional storage cost, multiple random permutations are then employed to train a series of classifiers for achieving better discrimination power. In addition, we introduce a sequential forward floating search (SFFS) algorithm to perform model selection, resulting in further performance improvement. Extensive experimental evaluations and comparative studies clearly demonstrate that the proposed Bayesian Hashing approach outperforms other peer methods in both accuracy and speed. We achieve state-of-the-art results on well-known face recognition benchmarks using compact binary codes with significantly reduced computational overload and storage cost.

Person re-identification concerns the matching of pedestrians across disjoint camera views. Due to the changes of viewpoints, lighting conditions and camera features, images of the same person from different views always appear differently, and thus feature representations across disjoint camera views of the same person follow different distributions. In this work, we propose an effective, low cost and easy-to-apply schema called the Mirror Representation, which embeds the view-specific feature transformation and enables alignment of the feature distributions across disjoint views for the same person. The proposed Mirror Representation is also designed to explicitly model the relation between different view-specific transformations and meanwhile control their discrepancy. With our Mirror Representation, we can enhance existing subspace/metric learning models significantly, and we particularly show that kernel marginal fisher analysis significantly outperforms the current state-of-the-art methods through extensive experiments on VIPeR, PRID450S and CUHK01.

Random feature map is popularly used to scale up kernel methods. However, employing a large number of mapped features to ensure an accurate approximation will still make the training time consuming. In this paper, we aim to improve the training efficiency of shift-invariant kernels by using fewer informative features without sacrificing precision. We propose a novel feature map method by extending Random Kitchen Sinks through fast data-dependent subspace embedding to generate the desired features. More specifically, we describe two algorithms with different tradeoffs on the running speed and accuracy, and prove that O(l) features induced by them are able to perform as accurately as O(l 2 ) features by other feature map methods. In addition, several experiments are conducted on the real-world datasets demonstrating the superiority of our proposed algorithms.

We present a novel model-metric co-learning (MMCL) methodology for sequence classification which learns in the model space -- each data item (sequence) is represented by a predictive model from a carefully designed model class. MMCL learning encourages sequences from the same class to be represented by ‘close’ model representations, well separated from those for different classes. Existing approaches to the problem either fit a single model to all the data, or a (predominantly linear) model on each sequence. We introduce a novel hybrid approach spanning the two extremes. The model class we use is a special form of adaptive high-dimensional non-linear state space model with a highly constrained and simple dynamic part. The dynamic part is identical for all data items and acts as a temporal filter providing a rich pool of dynamic features that can be selectively extracted by individual (static) linear readout mappings representing the sequences. Alongside learning the dynamic part, we also learn the global metric in the model readout space. Experiments on synthetic and benchmark data sets confirm the effectiveness of the algorithm compared to a variety of alternative methods.

We consider the problem of learning the optimal policy of an unknown Markov decision process (MDP) when expert demonstrations are available along with interaction samples. We build on classification-based policy iteration to perform a seamless integration of interaction and expert data, thus obtaining an algorithm which can benefit from both sources of information at the same time. Furthermore, we provide a full theoretical analysis of the performance across iterations providing insights on how the algorithm works. Finally, we report an empirical evaluation of the algorithm and a comparison with the state-of-the-art algorithms.