Sharif Digital Repository

Metric learning is a powerful approach for semi-supervised clustering. In this paper, a metric learning method considering both pairwise constraints and the geometrical structure of data is introduced for semi-supervised clustering. At first, a smooth metric is found (based on an optimization problem) using positive constraints as supervisory information. Then, an extension of this method employing both positive and negative constraints is introduced. As opposed to the existing methods, the extended method has the capability of considering both positive and negative constraints while considering the topological structure of data. The proposed metric learning method can improve performance of... 

In this paper, a multiple metric learning scheme for human pose estimation from a single image is proposed. Here, we focused on a big challenge of this problem which is; different 3D poses might correspond to similar inputs. To address this ambiguity, some Euclidean distance based approaches use prior knowledge or pose model that can work properly, provided that the model parameters are being estimated accurately. In the proposed method, the manifold of data is divided into several clusters and then, we learn a new metric for each partition by utilizing not only input features, but also their corresponding poses. The manifold clustering allows the decomposition of multiple manifolds into a... 

Distance metric learning is a powerful approach to deal with the clustering problem with side information. For semi-supervised clustering, usually a set of pairwise similarity and dissimilarity constraints is provided as supervisory information. Although some of the existing methods can use both equivalence (similarity) and inequivalence (dissimilarity) constraints, they are usually limited to learning a global Mahalanobis metric (i.e., finding a linear transformation). Moreover, they find metrics only according to the data points appearing in constraints, and cannot utilize information of other data points. In this paper, we propose a probabilistic metric learning algorithm which uses... 

In many real-world applications, data contain heterogeneous input modalities (e.g., web pages include images, text, etc.). Moreover, data such as images are usually described using different views (i.e. different sets of features). Learning a distance metric or similarity measure that originates from all input modalities or views is essential for many tasks such as content-based retrieval ones. In these cases, similar and dissimilar pairs of data can be used to find a better representation of data in which similarity and dissimilarity constraints are better satisfied. In this paper, we incorporate supervision in the form of pairwise similarity and/or dissimilarity constraints into... 

In many real-world applications (e.g. social media application), data usually consists of diverse input modalities that originates from various heterogeneous sources. Learning a similarity measure for such data is of great importance for vast number of applications such as classification, clustering, retrieval, etc. Defining an appropriate distance metric between data points with multiple modalities is a key challenge that has a great impact on the performance of many multimedia applications. Existing approaches for multi-modal distance metric learning only offer point estimation of the distance matrix and/or latent features, and can therefore be unreliable when the number of training... 

The problem of clustering with side information has received much recent attention and metric learning has been considered as a powerful approach to this problem. Until now, various metric learning methods have been proposed for semi-supervised clustering. Although some of the existing methods can use both positive (must-link) and negative (cannot-link) constraints, they are usually limited to learning a linear transformation (i.e., finding a global Mahalanobis metric). In this paper, we propose a framework for learning linear and non-linear transformations efficiently. We use both positive and negative constraints and also the intrinsic topological structure of data. We formulate our metric... 

Learning an appropriate distance measure under supervision of side information has become a topic of significant interest within machine learning community. In this paper, we address the problem of metric learning for constrained clustering by considering three important issues: (1) considering importance degree for constraints, (2) preserving the topological structure of data, and (3) preserving some natural distribution properties in the data. This work provides a unified way to handle different issues in constrained clustering by learning an appropriate distance measure. It has modeled the first issue by injecting the importance degree of constraints directly into an objective function.... 

Discriminative approaches to human pose estimation have became popular in recent years. These approaches face a big challenge: Similar inputs might correspond to very dissimilar poses. This property misleads the mapping functions which rely on the Euclidean distances in the input space. In this paper, we use the distances between the labels of the training data to learn a metric and map the input data to a space where this problem is minimized. Our mapping is linear and hence preserves the manifold structure of the input data. We benefit from the unlabeled data to estimate this manifold in the new space as a nearest neighbor graph. We finally utilize Tikhonov regularization to find a smooth... 

Real networks often exhibit nontrivial topological features that do not occur in random graphs. The need for synthesizing realistic networks has resulted in development of various network models. In this paper, we address the problem of selecting and calibrating the model that best fits a given target network. The existing model fitting approaches mostly suffer from sensitivity to network perturbations, lack of the parameter estimation component, dependency on the size of the networks, and low accuracy. To overcome these limitations, we considered a broad range of network features and employed machine learning techniques such as genetic algorithms, distance metric learning, nearest neighbor... 

In the last decade, there has been a growing interest in distance function learning for semi-supervised clustering settings. In addition to the earlier methods that learn Mahalanobis metrics (or equivalently, linear transformations), some nonlinear metric learning methods have also been recently introduced. However, these methods either allow limited choice of distance metrics yielding limited flexibility or learn nonparametric kernel matrices and scale very poorly (prohibiting applicability to medium and large data sets). In this paper, we propose a novel method that learns low-rank kernel matrices from pairwise constraints and unlabeled data. We formulate the proposed method as a trace... 

Distance metric plays an important role in many machine learning algorithms. Recently, there has been growing interest in distance metric learning for semi-supervised setting. In the last few years, many methods have been proposed for metric learning when pairwise similarity (must-link) and/or dissimilarity (cannot-link) constraints are available along with unlabeled data. Most of these methods learn a global Mahalanobis metric (or equivalently, a linear transformation). Although some recently introduced methods have devised nonlinear extensions of linear metric learning methods, they usually allow only limited forms of distance metrics and also can use only similarity constraints. In this... 

Distance metric has an important role in many machine learning algorithms. Recently, metric learning for semi-supervised algorithms has received much attention. For semi-supervised clustering, usually a set of pairwise similarity and dissimilarity constraints is provided as supervisory information. Until now, various metric learning methods utilizing pairwise constraints have been proposed. The existing methods that can consider both positive (must-link) and negative (cannot-link) constraints find linear transformations or equivalently global Mahalanobis metrics. Additionally, they find metrics only according to the data points appearing in constraints (without considering other data...