Spatial processes are typically used to analyse and predict geographic data. This paper adapts such models to predicting a user's interests (i.e., implicit item ratings) within a recommender system in the museum domain. We present the theoretical framework for a model based on Gaussian spatial processes, and discuss efficient algorithms for parameter estimation. Our model was evaluated with a real-world dataset collected by tracking visitors in a museum, attaining a higher predictive accuracy than state-of-the-art collaborative filters.

Feature selection can be conducted in a supervised or unsupervised manner, in terms of whether the label information We consider the problem of semi-supervised feature is utilized to guide the selection of relevant features. Generally, selection, where we are given a small amount supervised feature selection methods require a large of labeled examples and a large amount of unlabeled amount of labeled training data. It however could fail to identify examples. Since a small number of labeled the relevant features that are discriminative to different samples are usually insufficient for identifying the classes, provided the number of labeled samples is small. On relevant features, the critical problem arising from the other hand, while unsupervised feature selection methods semi-supervised feature selection is how to take could work well with unlabeled training data, they ignore advantage of the information underneath the unlabeled the label information and therefore are often unable to identify data. To address this problem, we propose the discriminative features. Given the high cost in manually a novel discriminative semi-supervised feature labeling data, and at the same time abundant unlabeled selection method based on the idea of manifold data are often easily accessible, it is desirable to develop feature regularization. The proposed method selects selection methods that are capable of exploiting both labeled features through maximizing the classification margin and unlabeled data.

The Universum sample, which is defined as the sample that doesn't belong to any of the classes the learning task concerns, has been proved to be helpful in both supervised and semi-supervised settings. The former works treat the Universum samples equally. Our research found that not all the Universum samples are helpful, and we propose a method to pick the informative ones, i.e., in-between Universum samples. We also set up a new semi-supervised framework to incorporate the in-between Universum samples. Empirical experiments show that our method outperforms the former ones.

Clustering, classification, and regression, are three major research topics in machine learning. So far, much work has been conducted in solving multiple instance classification and multiple instance regression problems, where supervised training patterns are given as bags and each bag consists of some instances. But the research on unsupervised multiple instance clustering is still limited . This paper formulates a novel Maximum Margin Multiple Instance Clustering problem for the multiple instance clustering task. To avoid solving a non-convex  optimization problem directly, M 3 IC is further relaxed, which enables an efficient optimization solution with a combination of Constrained Concave-Convex Procedure CCCP) and the Cutting Plane method. Furthermore, this paper analyzes some important properties of the proposed method and the relationship between the proposed method and some other related ones. An extensive set of empirical results demonstrate the advantages of the proposed method against existing research for both effectiveness and efficiency.

Ecosystem Informatics brings together mathematical and computational tools to address scientific and policy challenges in the ecosystem sciences. These challenges include novel sensors for collecting data, algorithms for automated data cleaning, learning methods for building statistical models from data and for fitting mechanistic models to data, and algorithms for designing optimal policies for biosphere management. This presentation discusses these challenges and then describes recent work on the first two of these--new methods for automated arthropod population counting and linear Gaussian DBNs for automated cleaning of sensor network data.

Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised learning tasks. In this paper, we propose a new algorithm for solving semi-supervised binary classification problem using sparse GP regression (GPR) models. It is closely related to semi-supervised learning based on support vector regression (SVR) and maximum margin clustering. The proposed algorithm is simple and easy to implement. It gives a sparse solution directly unlike the SVR based algorithm. Also, the hyperparameters are estimated easily without resorting to expensive cross-validation technique. Use of sparse GPR model helps in making the proposed algorithm scalable. Preliminary results on synthetic and real-world data sets demonstrate the efficacy of the new algorithm.

In this paper, we propose a new spectral clustering method, referred to as Spectral Embedded Clustering (SEC), to minimize the normalized cut criterion in spectral clustering as well as control the mismatch between the cluster assignment matrix and the low dimensional embedded representation of the data. SEC is based on the observation that the cluster assignment matrix of high dimensional data can be represented by a low dimensional linear mapping of data. We also discover the connection between SEC and other clustering methods, such as spectral clustering, Clustering with local and global regularization, K-means and Discriminative K-means. The experiments on many real-world data sets show that SEC significantly outperforms the existing spectral clustering methods as well as K-means clustering related methods.

Dimensionality reduction is an essential step in high-dimensional data analysis. Many dimensionality reduction algorithms have been applied successfully to multi-class and multi-label problems. They are commonly applied as a separate data preprocessing step before classification algorithms. In this paper, we study a joint learning framework in which we perform dimensionality reduction and multi-label classification simultaneously. We show that when the least squares loss is used in classification, this joint learning decouples into two separate components, i.e., dimensionality reduction followed by multi-label classification. This analysis partially justifies the current practice of a separate application of dimensionality reduction for classification problems. We extend our analysis using other loss functions, including the hinge loss and the squared hinge loss. We further extend the formulation to the more general case where the input data for different class labels may differ, overcoming the limitation of traditional dimensionality reduction algorithms. Experiments on benchmark data sets have been conducted to evaluate the proposed joint formulations.

This paper deals with evolutionary clustering, which refers to the problem of clustering data with distribution drifting along time. Starting from a density estimation view to clustering problems, we propose two general on-line frameworks. In the first framework, i.e., historical data dependent (HDD), current model distribution is designed to approximate both current and historical data distributions. In the second framework, i.e., historical model dependent (HMD), current model distribution is designed to approximate both current data distribution and historical model distribution. Both frameworks are based on the general exponential family mixture (EFM) model. As a result, all conventional clustering algorithms based on EFMs can be extended to evolutionary setting under the two frameworks. Empirical results validate the two frameworks.

Most of the existing metric learning methods are accomplished by exploiting pairwise constraints over the labeled data and frequently suffer from the insufficiency of training examples.  To learn a robust distance metric from few labeled examples, prior knowledge from unlabeled examples as well as the metrics previously derived from auxiliary data sets can be useful.  In this paper, we propose to leverage such auxiliary knowledge to assist distance metric learning, which is formulated following the regularized loss minimization principle.  Two algorithms are derived on the basis of manifold regularization and log-determinant divergence regularization technique, respectively, which can simultaneously exploit label information (i.e., the pairwise constraints over labeled data), unlabeled examples, and the metrics derived from auxiliary data sets.  The proposed methods directly manipulate the auxiliary metrics and require no raw examples from the auxiliary data sets, which make them efficient and flexible.  We conduct extensive evaluations to compare our approaches with a number of competing approaches on face recognition task.  The experimental results show that our approaches can derive reliable distance metrics from limited training examples and thus are superior in terms of accuracy and labeling efforts.