In this paper, we propose to represent and classify complicated objects. In order to represent the objects, we propose a multi-graph-view model which uses graphs constructed from multiple graph-views to represent an object. In addition, a bag based multi-graph model is further used to relax labeling by only requiring one label for a bag of graphs, which represent one object. In order to learn classification models, we propose a multi-graph-view bag learning algorithm (MGVBL), which aims to explore subgraph features from multiple graph-views for learning. By enabling a joint regularization across multiple graph-views, and enforcing labeling constraints at the bag and graph levels, MGVBL is able to discover most effective subgraph features across all graph-views for learning. Experiments on real-world learning tasks demonstrate the performance of MGVBL for complicated object classification.

Kernel-based methods, such as kernel k-means and kernel PCA, have been widely used in machine learning tasks. The performance of these methods critically depends on the selection of kernel functions; however, the challenge is that we usually do not know what kind of kernels is suitable for the given data and task in advance; this leads to research on multiple kernel learning, i.e. we learn a consensus kernel from multiple candidate kernels. Existing multiple kernel learning methods have difficulty in dealing with noises. In this paper, we propose a novel method for learning a robust yet low-rank kernel for clustering tasks. We observe that the noises of each kernel have specific structures, so we can make full use of them to clean multiple input kernels and then aggregate them into a robust, low-rank consensus kernel. The underlying optimization problem is hard to solve and we will show that it can be solved via alternating minimization, whose convergence is theoretically guaranteed. Experimental results on several benchmark data sets further demonstrate the effectiveness of our method.

Many clustering methods highly depend on extracted features. In this paper, we propose a joint optimization framework in terms of both feature extraction and discriminative clustering. We utilize graph regularized sparse codes as the features, and formulate sparse coding as the constraint for clustering. Two cost functions are developed based on entropy-minimization and maximum-margin clustering principles, respectively, as the objectives to be minimized. Solving such a bi-level optimization mutually reinforces both sparse coding and clustering steps.  Experiments on several benchmark datasets verify remarkable performance improvements led by the proposed joint optimization.

Traditional methods which the features form cluster structures, as can be seen in often rely on R.I.P or its relaxed variants. However, many machine learning [Lehiste, 1976] and computer vision in real applications, features are often correlated problems [Lan et al., 2013; Lowe, 2004]. Due to the fact that to each other, which makes these assumptions many features extractors are similar to each others and they too strong to be useful. In this paper, we reflect the characteristics of the same image, vision features study the sparse recovery problem in which the feature are often correlated and have cluster structures. This correlation matrix is strictly non-R.I.P.. We prove that is even stronger in those systems that have thousands when features exhibit cluster structures, which often to millions of features [Lan et al., 2013; Gan et al., 2015a; happens in real applications, we are able to recover 2015b].

Tuning hyper-parameters for large-scale matrix factorization (MF) is very time consuming and sometimes unacceptable. Intuitively, we want to tune hyper-parameters on small sub-matrix sample and then exploit them into the original large-scale matrix. However, most of existing MF methods are scale-variant, which means  the optimal hyper-parameters usually change with the different scale of matrices. To this end, in this paper we propose a scale-invariant parametric MF method, where a set of scale-invariant parameters is defined for model complexity regularization. Therefore, the proposed method can free us from tuning hyper-parameters on large-scale matrix, and achieve a good performance in a more efficient way. Extensive experiments on real-world dataset clearly validate both the effectiveness and efficiency of our method.

With the prevalence of mobile search nowadays, the benefits of mobile query recommendation are well recognized, which provide formulated queries sticking to users’ search intent. In this paper, we introduce the problem of query recommendation on mobile devices and model the user-location-query relations with a tensor representation. Unlike previous studies based on tensor decomposition, we study this problem via tensor function learning. That is, we learn the tensor function from the side information of users, locations and queries, and then predict users’ search intent. We develop an efficient alternating direction method of multipliers (ADMM) scheme to solve the introduced problem. We empirically evaluate our approach based on the mobile query dataset from Bing search engine in the city of Beijing, China, and show that our method can outperform several state-of-the-art approaches.

Tensor factorization methods provide a useful way to extract latent factors from complex multirelational data, and also for predicting missing data. Developing tensor factorization methods for massive tensors, especially when the data are binary- or count-valued (which is true of most real-world tensors), however, remains a challenge. We develop a scalable probabilistic tensor factorization framework that enables us to perform efficient factorization of massive binary and count tensor data. The framework is based on (i) the Polya-Gamma augmentation strategy which makes the model fully locally conjugate and allows closed-form parameter updates when data are binary- or count-valued; and (ii) an efficient online Expectation Maximization algorithm, which allows processing data in small mini-batches, and facilitates handling massive tensor data. Moreover, various types of constraints on the factor matrices (e.g., sparsity, non-negativity) can be incorporated under the proposed framework, providing good interpretability, which can be useful for qualitative analyses of the results. We apply the proposed framework on analyzing several binary- and count-valued real-world data sets.

Recently significant advances have been witnessed in the area of distributed word representations based on neural networks, which are also known as word embeddings. Among the new word embedding models, skip-gram negative sampling (SGNS) in the word2vec toolbox has attracted much attention due to its simplicity and effectiveness. However, the principles of SGNS remain not well understood, except for a recent work that explains SGNS as an implicit matrix factorization of the pointwise mutual information (PMI) matrix. In this paper, we provide a new perspective for further understanding SGNS. We point out that SGNS is essentially a representation learning method, which learns to represent the co-occurrence vector for a word. Based on the representation learning view, SGNS is in fact an explicit matrix factorization (EMF) of the words’ co-occurrence matrix. Furthermore, extended supervised word embedding can be established based on our proposed representation learning view.

Exploiting the information from multiple views can improve clustering accuracy. However, most  existing multi-view clustering algorithms are non-convex and are thus prone to becoming stuck into bad local minima, especially when there are outliers and missing data. To overcome this problem, we present a new multi-view self-paced learning (MSPL) algorithm for clustering, that  learns the multi-view model by not only progressing from 'easy'  to 'complex' examples, but also from 'easy'  to 'complex' views. Instead of binarily separating the examples or views into 'easy' and 'complex', we design a novel probabilistic smoothed weighting scheme. Employing multiple views for clustering and  defining complexity  across both examples and views are shown theoretically  to be beneficial to optimal clustering. Experimental results on toy and real-world data demonstrate the efficacy of the proposed algorithm.

This dissertation develops the intelligent-Coalition Formation framework for Humans and Robots (i-CiFHaR), an intelligent decision making frameworkfor multi-agent coalition formation. i-CiFHaR is a first of its kind that incorporates a library of coalition formation algorithms; employs unsupervised learning to mine crucial patterns among these algorithms; and leverages probabilistic reasoning to derive the most appropriate algorithm(s) to apply in accordance with multiple mission criteria. The dissertation also contributes to the state-of-the-art in swarm intelligence by addressing the search stagnation limitation of existing ant colony optimization algorithms (ACO) by integrating the simulated annealing mechanism. The experimental results demonstrate that the presented hybrid ACO algorithms significantly outperformed the best existing ACO approaches, when applied to three NP-complete optimization problems (e.g., traveling salesman problem, maximal clique problem, multi-agent coalition formation problem).