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2000, IEEE Signal Processing Magazine
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2014, Communications in Computer and Information Science
2012, 2012 IEEE International Workshop on Machine Learning for Signal Processing
2007
2012, IEEE Transactions on Multimedia
2018, Energies
Healthcare buildings exhibit a different electrical load predictability depending on their size and nature. Large hospitals behave similarly to small cities, whereas primary care centers are expected to have different consumption dynamics. In this work, we jointly analyze the electrical load predictability of a large hospital and that of its associated primary care center. An unsupervised load forecasting scheme using combined classic methods of principal component analysis (PCA) and autoregressive (AR) modeling, as well as a supervised scheme using orthonormal partial least squares (OPLS), are proposed. Both methods reduce the dimensionality of the data to create an efficient and low-complexity data representation and eliminate noise subspaces. Because the former method tended to underestimate the load and the latter tended to overestimate it in the large hospital, we also propose a convex combination of both to further reduce the forecasting error. The analysis of data from 7 years in the hospital and 3 years in the primary care center shows that the proposed low-complexity dynamic models are flexible enough to predict both types of consumption at practical accuracy levels.
2004, Neural Computation
2008, Machine Learning and Knowledge …
2000
In this document, we present methods to camera pose estimation from one single images,in a known,environment. The framework,of such methods comprises two stages, a learning step and an inference stage where given a new image we recover the exact camera position. This research work focus on achieving such a task with the help of lines and the Radon/Hough transform.
2012, Natural Language Engineering
2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)
2012
Abstract This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task.
The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems. Compared to frontal face recognition, which has been intensively studied and has gradually matured in the past few decades, pose-invariant face recognition (PIFR) remains a largely unsolved problem. However, PIFR is crucial to realizing the full potential of face recognition for real-world applications, since face recognition is intrinsically a passive biometric technology for recognizing uncooperative subjects. In this paper, we discuss the inherent difficulties in PIFR and present a comprehensive review of established techniques. Existing PIFR methods can be grouped into four categories, i.e., pose-robust feature extraction approaches, multi-view subspace learning approaches, face synthesis approaches, and hybrid approaches. The motivations, strategies, pros/cons, and performance of representative approaches are described and compared. Moreover, promising directions for future research are discussed. Final version: http://arxiv.org/pdf/1502.04383v3.pdf http://dl.acm.org/citation.cfm?id=2845089
2000, IEEE Reviews in Biomedical Engineering
2012, Computational Intelligence
2011
Fisher's linear discriminant analysis (LDA) is one of the most popular supervised linear dimensionality reduction methods. Unfortunately, LDA is not suitable for problems where the class labels are not available and only the spatial or temporal association of data samples is implicitly indicative of class membership. In this study, a new strategy for reducing LDA to Hotelling's canonical correlation analysis (CCA) is proposed.
2003, … -INTERNATIONAL WORKSHOP THEN …
2014, Journal of breath research
We define breathomics as the metabolomics study of exhaled air. It is a strongly emerging metabolomics research field that mainly focuses on health-related volatile organic compounds (VOCs). Since the amount of these compounds varies with health status, breathomics holds great promise to deliver non-invasive diagnostic tools. Thus, the main aim of breathomics is to find patterns of VOCs related to abnormal (for instance inflammatory) metabolic processes occurring in the human body. Recently, analytical methods for measuring VOCs in exhaled air with high resolution and high throughput have been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start with the detailed pre-processing pipelines for breathomics data obtained from gas-chromatography mass ...
2012, NeuroImage
2010, Image and Signal Processing for Remote Sensing XVI
2013, PLoS Computational Biology
2009, 2009 12th International Conference on Information Fusion
In the realm of multi-modal visual recognition, the reliability of the data acquisition system is often a concern due to the increased complexity of the sensors. One of the major issues is the accidental loss of one or more sensing channels, which poses a major challenge to current learning systems. In this paper, we examine one of these specific missing data problems, where we have a main modality/view along with an auxiliary modality/view present in the training data, but merely the main modality/view in the test data. To effectively leverage the auxiliary information to train a stronger classifier, we propose a collaborative auxiliary learning framework based on a new discriminative canonical correlation analysis. This framework reveals a common semantic space shared across both modalities/views through enforcing a series of nonlinear projections. Such projections automatically embed the discriminative cues hidden in both modalities/views into the common space, and better visual recognition is thus achieved on the test data. The efficacy of our proposed auxiliary learning approach is demonstrated through four challenging visual recognition tasks with different kinds of auxiliary information.
2009
Section 1.3 was incorrect, and 2.1 will be removed from further submissions. A rewritten version will be posted in the future.
2004, Bioinformatics
2010
Abstract Data recorded from multiple sources sometimes exhibit non-instantaneous couplings. For simple data sets, cross-correlograms may reveal the coupling dynamics. But when dealing with high-dimensional multivariate data there is no such measure as the cross-correlogram. We propose a simple algorithm based on Kernel Canonical Correlation Analysis (kCCA) that computes a multivariate temporal filter which links one data modality to another one.
2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
2007, Journal of Machine Learning Research
We examine an under-explored visual recognition problem, where we have a main view along with an auxiliary view of visual information present in the training data, but merely the main view is available in the test data. To effectively leverage the auxiliary view to train a stronger classifier, we propose a collaborative auxiliary learning framework based on a new discriminative canonical correlation analysis. This framework reveals a common semantic space shared across both views through enforcing a series of nonlinear projections. Such projections automatically embed the discriminative cues hidden in both views into the common space, and better visual recognition is thus achieved on the test data that stems from only the main view. The efficacy of our proposed auxiliary learning approach is demonstrated through three challenging visual recognition tasks with different kinds of auxiliary information.
2006, 2006 9th International Conference on Information Fusion
2015, Computers & Industrial Engineering
Metabolomics is the systematic study of the small-molecule profiles of biological samples produced by specific cellular processes. The high-throughput technologies used in metabolomic investigations generate datasets where variables are strongly correlated and redundancy is present in the data. Discovering the hidden information is a challenge, and suitable approaches for data analysis must be employed. Projection to latent structures regression (PLS) has successfully solved a large number of problems, from multivariate calibration to classification, becoming a basic tool of metabolomics. PLS2 is the most used implementation of PLS. Despite its success, PLS2 showed some limitations when the so called 'structured noise' affects the data. Suitable methods have been recently introduced to patch up these limitations. In this study, a comprehensive and up-to-date presentation of PLS2 focused on metabolomics is provided. After a brief discussion of the mathematical framework of PLS2, the post-transformation procedure is introduced as a basic tool for model interpretation. Orthogonally-constrained PLS2 is presented as strategy to include constraints in the model according to the experimental design. Two experimental datasets are investigated to show how PLS2 and its improvements work in practice.
2000, IEEE Transactions on Magnetics
2008, BMC Bioinformatics
2003, … European Conference on …