%0 Conference Paper
%B Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
%D 2010
%T Robust regression using sparse learning for high dimensional parameter estimation problems
%A Mitra, K.
%A Veeraraghavan,A.
%A Chellapa, Rama
%K algorithm;random
%K analysis;
%K combinatorial
%K complexity;least
%K complexity;parameter
%K consensus;robust
%K Estimation
%K estimation;polynomials;regression
%K learning;sparse
%K median
%K of
%K problem;cubic
%K problem;polynomial
%K problem;sparse
%K regression
%K representation;computational
%K sample
%K squares;parameter
%K TIME
%X Algorithms such as Least Median of Squares (LMedS) and Random Sample Consensus (RANSAC) have been very successful for low-dimensional robust regression problems. However, the combinatorial nature of these algorithms makes them practically unusable for high-dimensional applications. In this paper, we introduce algorithms that have cubic time complexity in the dimension of the problem, which make them computationally efficient for high-dimensional problems. We formulate the robust regression problem by projecting the dependent variable onto the null space of the independent variables which receives significant contributions only from the outliers. We then identify the outliers using sparse representation/learning based algorithms. Under certain conditions, that follow from the theory of sparse representation, these polynomial algorithms can accurately solve the robust regression problem which is, in general, a combinatorial problem. We present experimental results that demonstrate the efficacy of the proposed algorithms. We also analyze the intrinsic parameter space of robust regression and identify an efficient and accurate class of algorithms for different operating conditions. An application to facial age estimation is presented.
%B Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
%P 3846 - 3849
%8 2010/03//
%G eng
%R 10.1109/ICASSP.2010.5495830
%0 Conference Paper
%B 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)
%D 2010
%T The role of geometry in age estimation
%A Turaga,P.
%A Biswas,S.
%A Chellapa, Rama
%K age estimation
%K Aging
%K Biometrics
%K computational geometry
%K Face
%K Face Geometry
%K Facial animation
%K Feature extraction
%K function estimation problem
%K geometric face attributes
%K Geometry
%K Grassmann manifold
%K human face modeling
%K human face understanding
%K HUMANS
%K Mouth
%K regression
%K Regression analysis
%K SHAPE
%K Solid modeling
%K solid modelling
%K velocity vector
%X Understanding and modeling of aging in human faces is an important problem in many real-world applications such as biometrics, authentication, and synthesis. In this paper, we consider the role of geometric attributes of faces, as described by a set of landmark points on the face, in age perception. Towards this end, we show that the space of landmarks can be interpreted as a Grassmann manifold. Then the problem of age estimation is posed as a problem of function estimation on the manifold. The warping of an average face to a given face is quantified as a velocity vector that transforms the average to a given face along a smooth geodesic in unit-time. This deformation is then shown to contain important information about the age of the face. We show in experiments that exploiting geometric cues in a principled manner provides comparable performance to several systems that utilize both geometric and textural cues. We show results on age estimation using the standard FG-Net dataset and a passport dataset which illustrate the effectiveness of the approach.
%B 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)
%I IEEE
%P 946 - 949
%8 2010/03/14/19
%@ 978-1-4244-4295-9
%G eng
%R 10.1109/ICASSP.2010.5495292
%0 Conference Paper
%B Proceedings of the 2006 ACM SIGMOD international conference on Management of data
%D 2006
%T MauveDB: supporting model-based user views in database systems
%A Deshpande, Amol
%A Madden,Samuel
%K Query processing
%K regression
%K sensor networks
%K statistical models
%K uncertain data
%K views
%X Real-world data --- especially when generated by distributed measurement infrastructures such as sensor networks --- tends to be incomplete, imprecise, and erroneous, making it impossible to present it to users or feed it directly into applications. The traditional approach to dealing with this problem is to first process the data using statistical or probabilistic models that can provide more robust interpretations of the data. Current database systems, however, do not provide adequate support for applying models to such data, especially when those models need to be frequently updated as new data arrives in the system. Hence, most scientists and engineers who depend on models for managing their data do not use database systems for archival or querying at all; at best, databases serve as a persistent raw data store.In this paper we define a new abstraction called model-based views and present the architecture of MauveDB, the system we are building to support such views. Just as traditional database views provide logical data independence, model-based views provide independence from the details of the underlying data generating mechanism and hide the irregularities of the data by using models to present a consistent view to the users. MauveDB supports a declarative language for defining model-based views, allows declarative querying over such views using SQL, and supports several different materialization strategies and techniques to efficiently maintain them in the face of frequent updates. We have implemented a prototype system that currently supports views based on regression and interpolation, using the Apache Derby open source DBMS, and we present results that show the utility and performance benefits that can be obtained by supporting several different types of model-based views in a database system.
%B Proceedings of the 2006 ACM SIGMOD international conference on Management of data
%S SIGMOD '06
%I ACM
%C New York, NY, USA
%P 73 - 84
%8 2006///
%@ 1-59593-434-0
%G eng
%U http://doi.acm.org/10.1145/1142473.1142483
%R 10.1145/1142473.1142483