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Recently, sparse representation (SR) over a redundant dictionary has become a popular way of representing the data. It has been verified as an efficient and useful tool to promote the discrimination between signals. This work develops a joint learning approach to find the low dimensional discriminative features for high dimensional data. To avoid the high computational cost of direct sparse coding...
In this paper we aim at increasing the descriptive power of the covariance matrix, limited in capturing linear mutual dependencies between variables only. We present a rigorous and principled mathematical pipeline to recover the kernel trick for computing the covariance matrix, enhancing it to model more complex, non-linear relationships conveyed by the raw data. To this end, we propose Kernelized-COV,...
Mortality prediction of rare cancer types with a small number of high-dimensional samples is a challenging task. We propose a transfer learning model where both classes in rare cancers (target task) are modeled in a joint framework by transferring knowledge from the source task. The knowledge transfer is at the data level where only “related” data points are chosen to train the target task. Moreover,...
This paper presents a prediction based spatio-temporal seam carving scheme for video retargeting. It resizes the video maintaining appropriate balance between spatial and temporal coherence. In a video frame, the proposed approach finds a ‘temporal’ seam by using Kalman filter estimation and then modifies it with the help of ‘spatial’ seam considering both spatial and temporal coherency. Unlike image...
Computer-Aided Diagnosis (CAD) has witnessed a rapid growth over the past decade, providing a variety of automated tools for the analysis of medical images. In surgical pathology, such tools enhance the diagnosing capabilities of pathologists by allowing them to review and diagnose a larger number of cases daily. Geared towards developing such tools, the main goal of this paper is to identify useful...
Recently, hash algorithms catch amounts of sights in the field of machine learning. Most existing hash methods directly utilize a vector, which can be piped by the column of image matrix, as a unit and adopt some feature extraction functions to project the original data into generally shorter fixed-length values or characters. Then each of these projected real values is quantized or hashed into zero-one...
For images taken from very different viewpoints, we propose a new feature matching algorithm that provides accurate matches while preserving high matchability. Our method first synthesizes images by simulating the viewpoint changes. It then learns variation of local feature descriptors induced by the viewpoint changes. Finally, we robustly match feature descriptors by measuring the similarity using...
Multi-class learning from network data is an important but challenging problem with many applications, including malware detection in computer networks, user modeling in social networks, and protein function prediction in biological networks. Despite the extensive research on large multi-class learning, there are still numerous issues that have not been sufficiently addressed, such as efficiency of...
In this work we provide details on a new and effective approach able to generate Gaussian Mixture Models (GMMs) for the classification of aggregated time series. More specifically, our procedure can be applied to time series that are aggregated together by adding their features. The procedure takes advantage of the additive property of the Gaussians that complies with the additive property of the...
This paper presents a phonetically-aware joint density Gaussian mixture model (JD-GMM) framework for voice conversion that no longer requires parallel data from source speaker at the training stage. Considering that the phonetic level features contain text information which should be preserved in the conversion task, we propose a method that only concatenates phonetic discriminant features and spectral...
Change detection, in multi-temporal satellite imagery, seeks to discover relevant changes and to discard irrelevant ones. This task is usually achieved by modeling accurate decision criteria that capture the user's intention while being resilient to many irrelevant changes including acquisition conditions. Among existing change detection solutions, correlation-based models - such as canonical correlation...
The absolute orientation problem arises often in vision and robotics. Despite that robust algorithmic solutions exist for quite some time, they all rely on matrix factorizations such as eigen or singular value decomposition. These factorizations are relatively expensive to compute, therefore might become a performance bottleneck when absolute orientation needs to be repeatedly computed on low-end...
Traditional tensor decomposition methods, e.g., two dimensional principle component analysis (2DPCA) and two dimensional singular value decomposition (2DSVD), minimize mean square errors (MSE) and are sensitive to outliers. In this paper, we propose a new robust tensor factorization method using maximum correntropy criterion (MCC) to improve the robustness of traditional tensor decomposition methods...
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