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Feature selection with specific multivariate performance measures is the key to the success of many applications such as image retrieval and text classification. The existing feature selection methods are usually designed for classification error. In this paper, we propose a generalized sparse regularizer. Based on the proposed regularizer, we present a(More)
This paper focuses on learning a smooth skeleton structure from noisy data—an emerging topic in the fields of computer vision and computational biology. Many dimensionality reduction methods have been proposed, but none are specially designed for this purpose. To achieve this goal, we propose a unified probabilistic framework that directly models the(More)
Trefoil factor 3 (TFF3) is a member of the TFF-domain peptide family and essential in regulating cell migration and maintaining mucosal integrity in gastrointestinal tract. However, the role of TFF3 and its downstream regulating mechanisms in cancer cell migration remain unclear. We previously reported that TFF3 prolonged the up-regulation of Twist protein(More)
Due to the growing ubiquity of unlabeled data, learning with unlabeled data is attracting increasing attention in machine learning. In this paper, we propose a novel semi-supervised kernel learning method which can seamlessly combine man-ifold structure of unlabeled data and Regularized Least-Squares (RLS) to learn a new kernel. Interestingly , the new(More)
In the absence of the labeled samples in a domain referred to as target domain, Domain Adaptation (DA) techniques come in handy. Generally, DA techniques assume there are available source domains that share similar predictive function with the target domain. Two core challenges of DA typically arise, variance that exists between source and target domains,(More)
Conditional random fields (CRF) and structural support vector machines (structural SVM) are two state-of-the-art methods for structured prediction that captures the interdependencies among output variables. The success of these methods is attributed to the fact that their discriminative models are able to account for overlapping features on all input(More)
Automatic image annotation, which is usually formulated as a multi-label classification problem, is one of the major tools used to enhance the semantic understanding of web images. Many multimedia applications (e.g., tag-based image retrieval) can greatly benefit from image annotation. However, the insufficient performance of image annotation methods(More)
We propose an adaptive ensemble method to adapt coreference resolution across domains. This method has three features: (1) it can optimize for any user-specified objective measure ; (2) it can make document-specific prediction rather than rely on a fixed base model or a fixed set of base models; (3) it can automatically adjust the active ensemble members(More)
We present a new dimensionality reduction setting for a large family of real-world problems. Unlike traditional methods, the new setting aims to explicitly represent and learn an intrinsic structure from data in a high-dimensional space, which can greatly facilitate data visualization and scientific discovery in downstream analysis. We propose a new(More)