TY - CONF
T1 - Robust regression using sparse learning for high dimensional parameter estimation problems
T2 - Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Y1 - 2010
A1 - Mitra, K.
A1 - Veeraraghavan,A.
A1 - Chellapa, Rama
KW - algorithm;random
KW - analysis;
KW - combinatorial
KW - complexity;least
KW - complexity;parameter
KW - consensus;robust
KW - Estimation
KW - estimation;polynomials;regression
KW - learning;sparse
KW - median
KW - of
KW - problem;cubic
KW - problem;polynomial
KW - problem;sparse
KW - regression
KW - representation;computational
KW - sample
KW - squares;parameter
KW - TIME
AB - 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.
JA - Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
M3 - 10.1109/ICASSP.2010.5495830
ER -
TY - CONF
T1 - The role of geometry in age estimation
T2 - 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)
Y1 - 2010
A1 - Turaga,P.
A1 - Biswas,S.
A1 - Chellapa, Rama
KW - age estimation
KW - Aging
KW - Biometrics
KW - computational geometry
KW - Face
KW - Face Geometry
KW - Facial animation
KW - Feature extraction
KW - function estimation problem
KW - geometric face attributes
KW - Geometry
KW - Grassmann manifold
KW - human face modeling
KW - human face understanding
KW - HUMANS
KW - Mouth
KW - regression
KW - Regression analysis
KW - SHAPE
KW - Solid modeling
KW - solid modelling
KW - velocity vector
AB - 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.
JA - 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)
PB - IEEE
SN - 978-1-4244-4295-9
M3 - 10.1109/ICASSP.2010.5495292
ER -
TY - CONF
T1 - MauveDB: supporting model-based user views in database systems
T2 - Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Y1 - 2006
A1 - Deshpande, Amol
A1 - Madden,Samuel
KW - Query processing
KW - regression
KW - sensor networks
KW - statistical models
KW - uncertain data
KW - views
AB - 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.
JA - Proceedings of the 2006 ACM SIGMOD international conference on Management of data
T3 - SIGMOD '06
PB - ACM
CY - New York, NY, USA
SN - 1-59593-434-0
UR - http://doi.acm.org/10.1145/1142473.1142483
M3 - 10.1145/1142473.1142483
ER -