This paper addresses the problem of active learning of a multi-output Gaussian process (MOGP) model representing multiple types of coexisting correlated environmental phenomena. In contrast to existing works, our active learning problem involves selecting not just the most informative sampling locations to be observed but also the types of measurements at each selected location for minimizing the predictive uncertainty (i.e., posterior joint entropy) of a target phenomenon of interest given a sampling budget. Unfortunately, such an entropy criterion scales poorly in the numbers of candidate sampling locations and selected observations when optimized. To resolve this issue, we first exploit a structure common to sparse MOGP models for deriving a novel active learning criterion. Then, we exploit a relaxed form of submodularity property of our new criterion for devising a polynomial-time approximation algorithm that guarantees a constant-factor approximation of that achieved by the optimal set of selected observations. Empirical evaluation on real-world datasets shows that our proposed approach outperforms existing algorithms for active learning of MOGP and single-output GP models.

Graph-based Semi-Supervised learning is one of the most popular and successful semi-supervised learning methods. Typically, it predicts the labels of unlabeled data by minimizing a quadratic objective induced by the graph, which is unfortunately a procedure of polynomial complexity in the sample size $n$. In this paper, we address this scalability issue by proposing a method that approximately solves the quadratic objective in nearly linear time. The method consists of two steps: it first approximates a graph by a minimum spanning tree, and then solves the tree-induced quadratic objective function in O(n) time which is the main contribution of this work. Extensive experiments show the significant scalability improvement over existing scalable semi-supervised learning methods.

In this paper, we present an accelerated numerical method based on random projection for sparse linear regression. Previous studies have shown that under appropriate conditions, gradient-based methods enjoy a geometric convergence rate when applied to this problem. However, the time complexity of evaluating the gradient is as large as $\mathcal{O}(nd)$, where $n$ is the number of data points and $d$ is the dimensionality, making those methods inefficient for large-scale and high-dimensional dataset. To address this limitation, we first utilize random projection to find a rank-$k$ approximator for the data matrix, and reduce the cost of gradient evaluation to $\mathcal{O}(nk+dk)$, a significant improvement when $k$ is much smaller than $d$ and $n$. Then, we solve the sparse linear regression problem via a proximal gradient method with a homotopy strategy to generate sparse intermediate solutions. Theoretical analysis shows that our method also achieves a global geometric convergence rate, and moreover the sparsity of all the intermediate solutions are well-bounded over the iterations. Finally, we conduct experiments to demonstrate the efficiency of the proposed method.

In many learning tasks with structural properties, structured sparse modeling usually leads to better interpretability and higher generalization performance. While great efforts have focused on the convex regularization, recent studies show that nonconvex regularizers can outperform their convex counterparts in many situations. However, the resulting nonconvex optimization problems are still challenging, especially for the structured sparsity-inducing regularizers. In this paper, we propose a splitting method for solving nonconvex structured sparsity optimization problems. The proposed method alternates between a gradient step and an easily solvable proximal step, and thus enjoys low per-iteration computational complexity. We prove that the whole sequence generated by the proposed method converges to a critical point with at least sublinear convergence rate, relying on the Kurdyka-Łojasiewicz inequality. Experiments on both simulated and real-world data sets demonstrate the efficiency and efficacy of the proposed method.

Kernel Principal Component Analysis (PCA) is a popular extension of PCA which is able to find nonlinear patterns from data. However, the application of kernel PCA to large-scale problems remains a big challenge, due to its quadratic space complexity and cubic time complexity in the number of examples. To address this limitation, we utilize techniques from stochastic optimization to solve kernel PCA with linear space and time complexities per iteration. Specifically, we formulate it as a stochastic composite optimization problem, where a nuclear norm regularizer is introduced to promote low-rankness, and then develop a simple algorithm based on stochastic proximal gradient descent. During the optimization process, the proposed algorithm always maintains a low-rank factorization of iterates that can be conveniently held in memory. Compared to previous iterative approaches, a remarkable property of our algorithm is that it is equipped with an explicit rate of convergence. Theoretical analysis shows that the solution of our algorithm converges to the optimal one at an O(1/T) rate, where T is the number of iterations.

Most distance metric learning (DML) approaches focus on learning a Mahalanobis metric for measuring distances between examples. However, for particular feature representations, e.g., histogram features like BOW and SPM, Mahalanobis metric could not model the correlations between these features well. In this work, we define a non-Mahalanobis distance for histogram features, via Expected Hitting Time (EHT) of Markov Chain, which implicitly considers the high-order feature relationships between different histogram features. The EHT based distance is parameterized by transition probabilities of Markov Chain, we consequently propose a novel type of distance learning approach (LED, Learning Expected hitting time Distance) to learn appropriate transition probabilities for EHT based distance. We validate the effectiveness of LED on a series of real-world datasets. Moreover, experiments show that the learned transition probabilities are with good comprehensibility.

We consider the problem of approximating order-constrained transitive distance (OCTD) and its clustering applications. Given any pairwise data, transitive distance (TD) is defined as the smallest possible "gap" on the set of paths connecting them. While such metric definition renders significant capability of addressing elongated clusters, it is sometimes also an over-simplified representation which loses necessary regularization on cluster structure and overfits to short links easily. As a result, conventional TD often suffers from degraded performance given clusters with "thick" structures. Our key intuition is that the maximum (path) order, which is the maximum number of nodes on a path, controls the level of flexibility. Reducing this order benefits the clustering performance by finding a trade-off between flexibility and regularization on cluster structure. Unlike TD, finding OCTD becomes an intractable problem even though the number of connecting paths is reduced. We therefore propose a fast approximation framework, using random samplings to generate multiple diversified TD matrices and a pooling to output the final approximated OCTD matrix. Comprehensive experiments on toy, image and speech datasets show the excellent performance of OCTD, surpassing TD with significant gains and giving state-of-the-art performance on several datasets.

Many randomized heuristic derivative-free optimization methods share a framework that iteratively learns a model for promising search areas and samples solutions from the model. This paper studies a particular setting of such framework, where the model is implemented by a classification model discriminating good solutions from bad ones. This setting allows a general theoretical characterization, where critical factors to the optimization are discovered. We also prove that optimization problems with Local Lipschitz continuity can be solved in polynomial time by proper configurations of this framework. Following the critical factors, we propose the randomized coordinate shrinking classification algorithm to learn the model, forming the RACOS algorithm, for optimization in continuous and discrete domains. Experiments on the testing functions as well as on the machine learning tasks including spectral clustering and classification with Ramp loss demonstrate the effectiveness of RACOS.

Matrix completion is to recover missing/unobserved values of a data matrix from very limited observations. Due to widely potential applications, it has received growing interests in fields from machine learning, data mining, to collaborative filtering and computer vision. To ensure the successful recovery of missing values, most existing matrix completion algorithms utilise the low-rank assumption, i.e., the fully observed data matrix has a low rank, or equivalently the columns of the matrix can be linearly represented by a few numbers of basis vectors. Although such low-rank assumption applies generally in practice, real-world data can process much richer structural information. In this paper, we present a new model for matrix completion, motivated by the separability assumption of nonnegative matrices from the recent literature of matrix factorisations: there exists a set of columns of the matrix such that the resting columns can be represented by their convex combinations. Given the separability property, which holds reasonably for many applications, our model provides a more accurate matrix completion than the low-rank based algorithms. Further, we derives a scalable algorithm to solve our matrix completion model, which utilises a randomised method to select the basis columns under the separability assumption and a coordinate gradient based method to automatically deal with the structural constraints in optimisation. Compared to the state-of-the-art algorithms, the proposed matrix completion model achieves competitive results on both synthetic and real datasets.

Semi-supervised learning is proposed to exploit both labeled and unlabeled data. However, as the scale of data in real world applications increases significantly, conventional semi-supervised algorithms usually lead to massive computational cost and cannot be applied to large scale datasets. In addition, label noise is usually present in the practical applications due to human annotation, which very likely results in remarkable degeneration of performance in semi-supervised methods. To address these two challenges, in this paper, we propose an efficient RObust Semi-Supervised Ensemble Learning (ROSSEL) method, which generates pseudo-labels for unlabeled data using a set of weak annotators, and combines them to approximate the ground-truth labels to assist semi-supervised learning. We formulate the weighted combination process as a multiple label kernel learning (MLKL) problem which can be solved efficiently. Compared with other semi-supervised learning algorithms, the proposed method has linear time complexity. Extensive experiments on five benchmark datasets demonstrate the superior effectiveness, efficiency and robustness of the proposed algorithm.