@conference {12437,
	title = {Domain adaptation for object recognition: An unsupervised approach},
	booktitle = {2011 IEEE International Conference on Computer Vision (ICCV)},
	year = {2011},
	month = {2011/11/06/13},
	pages = {999 - 1006},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Adapting the classifier trained on a source domain to recognize instances from a new target domain is an important problem that is receiving recent attention. In this paper, we present one of the first studies on unsupervised domain adaptation in the context of object recognition, where we have labeled data only from the source domain (and therefore do not have correspondences between object categories across domains). Motivated by incremental learning, we create intermediate representations of data between the two domains by viewing the generative subspaces (of same dimension) created from these domains as points on the Grassmann manifold, and sampling points along the geodesic between them to obtain subspaces that provide a meaningful description of the underlying domain shift. We then obtain the projections of labeled source domain data onto these subspaces, from which a discriminative classifier is learnt to classify projected data from the target domain. We discuss extensions of our approach for semi-supervised adaptation, and for cases with multiple source and target domains, and report competitive results on standard datasets.},
	keywords = {Data models, data representations, discriminative classifier, Feature extraction, Grassmann manifold, image sampling, incremental learning, labeled source domain, Manifolds, measurement, object category, Object recognition, Principal component analysis, sampling points, semisupervised adaptation, target domain, underlying domain shift, unsupervised approach, unsupervised domain adaptation, Unsupervised learning, vectors},
	isbn = {978-1-4577-1101-5},
	doi = {10.1109/ICCV.2011.6126344},
	author = {Gopalan,R. and Ruonan Li and Chellapa, Rama}
}
@article {19368,
	title = {Structural and dynamic determinants of ligand binding and regulation of cyclin-dependent kinase 5 by pathological activator p25 and inhibitory peptide CIP.},
	journal = {Journal of molecular biology},
	volume = {401},
	year = {2010},
	month = {2010 Aug 20},
	pages = {478-92},
	abstract = {The crystal structure of the cdk5/p25 complex has provided information on possible molecular mechanisms of the ligand binding, specificity, and regulation of the kinase. Comparative molecular dynamics simulations are reported here for physiological conditions. This study provides new insight on the mechanisms that modulate such processes, which may be exploited to control pathological activation by p25. The structural changes observed in the kinase are stabilized by a network of interactions involving highly conserved residues within the cyclin-dependent kinase (cdk) family. Collective motions of the proteins (cdk5, p25, and CIP) and their complexes are identified by principal component analysis, revealing two conformational states of the activation loop upon p25 complexation, which are absent in the uncomplexed kinase and not apparent from the crystal. Simulations of the uncomplexed inhibitor CIP show structural rearrangements and increased flexibility of the interfacial loop containing the critical residue E240, which becomes fully hydrated and available for interactions with one of several positively charged residues in the kinase. These changes provide a rationale for the observed high affinity and enhanced inhibitory action of CIP when compared to either p25 or the physiological activators of cdk5.},
	keywords = {Crystallography, X-Ray, Cyclin-Dependent Kinase 5, Cyclin-Dependent Kinase Inhibitor Proteins, HUMANS, Ligands, molecular dynamics simulation, Nerve Tissue Proteins, Principal component analysis, Protein Binding, Protein Conformation},
	issn = {1089-8638},
	doi = {10.1016/j.jmb.2010.06.040},
	author = {Cardone, Antonio and Hassan, S A and Albers,R.W. and Sriram,R.D. and Pant,H.C.}
}
@article {17902,
	title = {Statistical geometry representation for efficient transmission and rendering},
	journal = {ACM Transactions on Graphics},
	volume = {24},
	year = {2005},
	month = {2005/04//},
	pages = {348 - 373},
	abstract = {Traditional geometry representations have focused on representing the details of the geometry in a deterministic fashion. In this article we propose a statistical representation of the geometry that leverages local coherence for very large datasets. We show how the statistical analysis of a densely sampled point model can be used to improve the geometry bandwidth bottleneck, both on the system bus and over the network as well as for randomized rendering, without sacrificing visual realism. Our statistical representation is built using a clustering-based hierarchical principal component analysis (PCA) of the point geometry. It gives us a hierarchical partitioning of the geometry into compact local nodes representing attributes such as spatial coordinates, normal, and color. We pack this information into a few bytes using classification and quantization. This allows our representation to directly render from compressed format for efficient remote as well as local rendering. Our representation supports both view-dependent and on-demand rendering. Our approach renders each node using quasi-random sampling utilizing the probability distribution derived from the PCA analysis. We show many benefits of our approach: (1) several-fold improvement in the storage and transmission complexity of point geometry; (2) direct rendering from compressed data; and (3) support for local and remote rendering on a variety of rendering platforms such as CPUs, GPUs, and PDAs.},
	keywords = {network graphics, Point-based rendering, Principal component analysis, programmable GPU, progressive transmission, quasi-random numbers, view-dependent rendering},
	isbn = {0730-0301},
	doi = {10.1145/1061347.1061356},
	url = {http://doi.acm.org/10.1145/1061347.1061356},
	author = {Kalaiah,Aravind and Varshney, Amitabh}
}
@conference {16944,
	title = {A Rank-by-Feature Framework for Unsupervised Multidimensional Data Exploration Using Low Dimensional Projections},
	booktitle = {IEEE Symposium on Information Visualization, 2004. INFOVIS 2004},
	year = {2004},
	month = {2004///},
	pages = {65 - 72},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Exploratory analysis of multidimensional data sets is challenging because of the difficulty in comprehending more than three dimensions. Two fundamental statistical principles for the exploratory analysis are (1) to examine each dimension first and then find relationships among dimensions, and (2) to try graphical displays first and then find numerical summaries (D.S. Moore, (1999). We implement these principles in a novel conceptual framework called the rank-by-feature framework. In the framework, users can choose a ranking criterion interesting to them and sort 1D or 2D axis-parallel projections according to the criterion. We introduce the rank-by-feature prism that is a color-coded lower-triangular matrix that guides users to desired features. Statistical graphs (histogram, boxplot, and scatterplot) and information visualization techniques (overview, coordination, and dynamic query) are combined to help users effectively traverse 1D and 2D axis-parallel projections, and finally to help them interactively find interesting features},
	keywords = {axis-parallel projections, boxplot, color-coded lower-triangular matrix, computational complexity, computational geometry, Computer displays, Computer science, Computer vision, Data analysis, data mining, data visualisation, Data visualization, Displays, dynamic query, Educational institutions, exploratory data analysis, feature detection, feature detection/selection, Feature extraction, feature selection, graph theory, graphical displays, histogram, Information Visualization, interactive systems, Laboratories, Multidimensional systems, Principal component analysis, rank-by-feature prism, scatterplot, statistical analysis, statistical graphics, statistical graphs, unsupervised multidimensional data exploration, very large databases},
	isbn = {0-7803-8779-3},
	doi = {10.1109/INFVIS.2004.3},
	author = {Seo,J. and Shneiderman, Ben}
}
@conference {19649,
	title = {A Rao-Blackwellized particle filter for EigenTracking},
	booktitle = {Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004},
	volume = {2},
	year = {2004},
	month = {2004/06//},
	pages = {II - 980-II-986 Vol.2},
	abstract = {Subspace representations have been a popular way to model appearance in computer vision. In Jepson and Black{\textquoteright}s influential paper on EigenTracking, they were successfully applied in tracking. For noisy targets, optimization-based algorithms (including EigenTracking) often fail catastrophically after losing track. Particle filters have recently emerged as a robust method for tracking in the presence of multi-modal distributions. To use subspace representations in a particle filter, the number of samples increases exponentially as the state vector includes the subspace coefficients. We introduce an efficient method for using subspace representations in a particle filter by applying Rao-Blackwellization to integrate out the subspace coefficients in the state vector. Fewer samples are needed since part of the posterior over the state vector is analytically calculated. We use probabilistic principal component analysis to obtain analytically tractable integrals. We show experimental results in a scenario in which we track a target in clutter.},
	keywords = {analytically tractable integrals, Computer vision, EigenTracking, Filters, Gaussian processes, modal analysis, multi-modal distributions, NOISE, noisy targets, optimisation, optimization-based algorithms, Particle filters, Particle measurements, Particle tracking, Principal component analysis, probabilistic principal component analysis, Rao-Blackwellized particle filter, Robustness, SHAPE, State estimation, state vector, subspace coefficients, Subspace representations, target tracking, vectors},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@conference {18311,
	title = {Estimation of composite object and camera image motion},
	booktitle = {The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999},
	volume = {1},
	year = {1999},
	month = {1999///},
	pages = {190-197 vol.1 - 190-197 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {An approach for estimating composite independent object and camera image motions is proposed. The approach employs spatio-temporal flow models learned through observing typical movements of the object, to decompose image motion into independent object and camera motions. The spatio-temporal flow models of the object motion are represented as a set of orthogonal flow bases that are learned using principal component analysis of instantaneous flow measurements from a stationary camera. These models are then employed in scenes with a moving camera to extract motion trajectories relative to those learned. The performance of the algorithm is demonstrated on several image sequences of rigid and articulated bodies in motion},
	keywords = {articulated bodies, camera image motion, camera image motions, CAMERAS, composite independent object, composite object estimation, Computer vision, Educational institutions, Image sequences, instantaneous flow measurements, Laboratories, Layout, Motion analysis, Motion estimation, motion trajectories, orthogonal flow bases, Principal component analysis, spatio-temporal flow models, tracking, Vehicles},
	isbn = {0-7695-0164-8},
	doi = {10.1109/ICCV.1999.791217},
	author = {Yacoob,Yaser and Davis, Larry S.}
}
@conference {18321,
	title = {Learning parameterized models of image motion},
	booktitle = {Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on},
	year = {1997},
	month = {1997/06//},
	pages = {561 - 567},
	abstract = {A framework for learning parameterized models of optical flow from image sequences is presented. A class of motions is represented by a set of orthogonal basis flow fields that are computed from a training set using principal component analysis. Many complex image motions can be represented by a linear combination of a small number of these basis flows. The learned motion models may be used for optical flow estimation and for model-based recognition. For optical flow estimation we describe a robust, multi-resolution scheme for directly computing the parameters of the learned flow models from image derivatives. As examples we consider learning motion discontinuities, non-rigid motion of human mouths, and articulated human motion},
	keywords = {image motion, Image sequences, learning, learning (artificial intelligence), model-based recognition, Motion estimation, multi-resolution scheme, non-rigid motion, optical flow, optical flow estimation, parameterized models, Principal component analysis, training set},
	doi = {10.1109/CVPR.1997.609381},
	author = {Black,M. J and Yacoob,Yaser and Jepson,A. D and Fleet,D. J}
}
