Embedded feature selection is effective when both prediction and interpretation are needed. The Lasso and its extensions are standard methods for selecting a subset of features while optimizing a prediction function. In this paper, we are interested in embedded feature selection for multidimensional data, wherein (1) there is no need to reshape the multidimensional data into vectors and (2) structural information from multiple dimensions are taken into account. Our main contribution is a new method called Regularized multilinear regression and selection (Remurs) for automatically selecting a subset of features while optimizing prediction for multidimensional data. Both nuclear norm and the ℓ 1 -norm are carefully incorporated to derive a multi-block optimization algorithm with proved convergence. In particular, Remurs is motivated by fMRI analysis where the data are multidimensional and it is important to find the connections of raw brain voxels with functional activities. Experiments on synthetic and real data show the advantages of Remurs compared to Lasso, Elastic Net, and their multilinear extensions.

Semi-supervised learning (SSL) concerns how to improve performance via the usage of unlabeled data. Recent studies indicate that the usage of unlabeled data might even deteriorate performance. Although some proposals have been developed to alleviate such a fundamental challenge for semi-supervised classification, the efforts on semi-supervised regression (SSR) remain to be limited. In this work we consider the learning of a safe prediction from multiple semi-supervised regressors, which is not worse than a direct supervised learner with only labeled data. We cast it as a geometric projection issue with an efficient algorithm. Furthermore, we show that the proposal is provably safe and has already achieved the maximal performance gain, if the ground-truth label assignment is realized by a convex linear combination of base regressors. This provides insight to help understand safe SSR. Experimental results on a broad range of datasets validate the effectiveness of our proposal.

Neural network language models (NNLMs) have attracted a lot of attention recently. In this paper, we present a training method that can incrementally train the hierarchical softmax function for NNMLs. We split the cost function to model old and update corpora separately, and factorize the objective function for the hierarchical softmax. Then we provide a new stochastic gradient based method to update all the word vectors and parameters, by comparing the old tree generated based on the old corpus and the new tree generated based on the combined (old and update) corpus. Theoretical analysis shows that the mean square error of the parameter vectors can be bounded by a function of the number of changed words related to the parameter node. Experimental results show that incremental training can save a lot of time. The smaller the update corpus is, the faster the update training process is, where an up to 30 times speedup has been achieved. We also use both word similarity/relatedness tasks and dependency parsing task as our benchmarks to evaluate the correctness of the updated word vectors.

We address the problem of zero-shot event recognition in consumer videos. An event usually consists of multiple human-human and human-object interactions over a relative long period of time. A common approach proceeds by representing videos with banks of object and action concepts, but requires additional user inputs to specify the desired concepts per event. In this paper, we provide a fully automatic algorithm to select representative and reliable concepts for event queries. This is achieved by discovering event composition knowledge (DECK) from web images. To evaluate our proposed method, we use the standard zero-shot event detection protocol (ZeroMED), but also introduce a novel zero-shot event recounting (ZeroMER) problem to select supporting evidence of the events. Our ZeroMER formulation aims to select video snippets that are relevant and diverse. Evaluation on the challenging TRECVID MED dataset show that our proposed method achieves promising results on both tasks.

Developed so far, multi-document summarization has reached its bottleneck due to the lack of sufficient training data and diverse categories of documents. Text classification just makes up for these deficiencies. In this paper, we propose a novel summarization system called TCSum, which leverages plentiful text classification data to improve the performance of multi-document summarization. TCSum projects documents onto distributed representations which act as a bridge between text classification and summarization. It also utilizes the classification results to produce summaries of different styles. Extensive experiments on DUC generic multi-document summarization datasets show that, TCSum can achieve the state-of-the-art performance without using any hand-crafted features and has the capability to catch the variations of summary styles with respect to different text categories.

Regularized empirical risk minimization (R-ERM) is an important branch of machine learning, since it constrains the capacity of the hypothesis space and guarantees the generalization ability of the learning algorithm. Two classic proximal optimization algorithms, i.e., proximal stochastic gradient descent (ProxSGD) and proximal stochastic coordinate descent (ProxSCD) have been widely used to solve the R-ERM problem. Recently, variance reduction technique was proposed to improve ProxSGD and ProxSCD, and the corresponding ProxSVRG and ProxSVRCD have better convergence rate. These proximal algorithms with variance reduction technique have also achieved great success in applications at small and moderate scales. However, in order to solve large-scale R-ERM problems and make more practical impacts, the parallel versions of these algorithms are sorely needed. In this paper, we propose asynchronous ProxSVRG (Async-ProxSVRG) and asynchronous ProxSVRCD (Async-ProxSVRCD) algorithms, and prove that Async-ProxSVRG can achieve near linear speedup when the training data is sparse, while Async-ProxSVRCD can achieve near linear speedup regardless of the sparse condition, as long as the number of block partitions are appropriately set. We have conducted experiments on a regularized logistic regression task. The results verified our theoretical findings and demonstrated the practical efficiency of the asynchronous stochastic proximal algorithms with variance reduction.

The Cooperative Target Observation (CTO) problem has been of great interest in the multi-agents and robotics literature due to the problem being at the core of a number of applications including surveillance. In CTO problem, the observer agents attempt to maximize the collective time during which each moving target is being observed by at least one observer in the area of interest. However, most of the prior works for the CTO problem consider the targets movement to be Randomized. Given our focus on surveillance domain, we modify this assumption to make the targets strategic and present two target strategies namely Straight-line strategy and Controlled Randomization strategy. We then modify the observer strategy proposed in the literature based on the K-means algorithm to introduce five variants and provide experimental validation. In surveillance domain, it is often reasonable to assume that the observers may themselves be a subject of observation for a variety of purposes by unknown adversaries whose model may not be known. Randomizing the observers actions can help to make their target observation strategy less predictable. As the fifth variant, we therefore introduce Adjustable Randomization into the best performing observer strategy where the observer can adjust the expected loss in reward due to randomization depending on the situation.

Circular variables arise in a multitude of data-modelling contexts ranging from robotics to the social sciences, but they have been largely overlooked by the machine learning community. This paper partially redresses this imbalance by extending some standard probabilistic modelling tools to the circular domain. First we introduce a new multivariate distribution over circular variables, called the multivariate Generalised von Mises (mGvM) distribution. This distribution can be constructed by restricting and renormalising a general multivariate Gaussian distribution to the unit hyper-torus. Previously proposed multivariate circular distributions are shown to be special cases of this construction. Second, we introduce a new probabilistic model for circular regression inspired by Gaussian Processes, and a method for probabilistic Principal Component Analysis with circular hidden variables. These models can leverage standard modelling tools (e.g. kernel functions and automatic relevance determination). Third, we show that the posterior distribution in these models is a mGvM distribution which enables development of an efficient variational free-energy scheme for performing approximate inference and approximate maximum-likelihood learning.

Conditional gradient descent, aka the Frank-Wolfe algorithm,regains popularity in recent years. The key advantage of Frank-Wolfe is that at each step the expensive projection is replaced with a much more efficient linear optimization step. Similar to gradient descent, the loss function of Frank-Wolfe scales with the data size. Training on big data poses a challenge for researchers. Recently, stochastic Frank-Wolfe methods have been proposed to solve the problem, but they do not perform well in practice. In this work, we study the problem of approximating the Frank-Wolfe algorithm on the large-scale multi-class classification problem which is a typical application of the Frank-Wolfe algorithm. We present a simple but effective method employing internal structure of data to approximate Frank-Wolfe on the large-scale multiclass classification problem. Empirical results verify that our method outperforms the state-of-the-art stochastic projection free methods.

With the development of the information technology, the amount of data, e.g. text, image and video, has been increased rapidly. Efficiently clustering those large scale data sets is a challenge. To address this problem, this paper proposes a novel co-clustering method named bilateral k-means algorithm (BKM) for fast co-clustering. Different from traditional k-means algorithms, the proposed method has two indicator matrices P and Q and a diagonal matrix S to be solved, which represent the cluster memberships of samples and features, and the co-cluster centres, respectively. Therefore, it could implement different clustering tasks on the samples and features simultaneously. We also introduce an effective approach to solve the proposed method, which involves less multiplication. The computational complexity is analyzed. Extensive experiments on various types of data sets are conducted. Compared with the state-of-the-art clustering methods, the proposed BKM not only has faster computational speed, but also achieves promising clustering results.