297 research outputs found
A deep matrix factorization method for learning attribute representations
Semi-Non-negative Matrix Factorization is a technique that learns a
low-dimensional representation of a dataset that lends itself to a clustering
interpretation. It is possible that the mapping between this new representation
and our original data matrix contains rather complex hierarchical information
with implicit lower-level hidden attributes, that classical one level
clustering methodologies can not interpret. In this work we propose a novel
model, Deep Semi-NMF, that is able to learn such hidden representations that
allow themselves to an interpretation of clustering according to different,
unknown attributes of a given dataset. We also present a semi-supervised
version of the algorithm, named Deep WSF, that allows the use of (partial)
prior information for each of the known attributes of a dataset, that allows
the model to be used on datasets with mixed attribute knowledge. Finally, we
show that our models are able to learn low-dimensional representations that are
better suited for clustering, but also classification, outperforming
Semi-Non-negative Matrix Factorization, but also other state-of-the-art
methodologies variants.Comment: Submitted to TPAMI (16-Mar-2015
Discriminant Projective Non-Negative Matrix Factorization
Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers W-T X as their coefficients, i.e., X approximate to WWT X. Since PNM
Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition
This paper presents a novel quadratic projection based feature extraction
framework, where a set of quadratic matrices is learned to distinguish each
class from all other classes. We formulate quadratic matrix learning (QML) as a
standard semidefinite programming (SDP) problem. However, the con- ventional
interior-point SDP solvers do not scale well to the problem of QML for
high-dimensional data. To solve the scalability of QML, we develop an efficient
algorithm, termed DualQML, based on the Lagrange duality theory, to extract
nonlinear features. To evaluate the feasibility and effectiveness of the
proposed framework, we conduct extensive experiments on biometric recognition.
Experimental results on three representative biometric recogni- tion tasks,
including face, palmprint, and ear recognition, demonstrate the superiority of
the DualQML-based feature extraction algorithm compared to the current
state-of-the-art algorithm
3D Face Recognition with Sparse Spherical Representations
This paper addresses the problem of 3D face recognition using simultaneous
sparse approximations on the sphere. The 3D face point clouds are first aligned
with a novel and fully automated registration process. They are then
represented as signals on the 2D sphere in order to preserve depth and geometry
information. Next, we implement a dimensionality reduction process with
simultaneous sparse approximations and subspace projection. It permits to
represent each 3D face by only a few spherical functions that are able to
capture the salient facial characteristics, and hence to preserve the
discriminant facial information. We eventually perform recognition by effective
matching in the reduced space, where Linear Discriminant Analysis can be
further activated for improved recognition performance. The 3D face recognition
algorithm is evaluated on the FRGC v.1.0 data set, where it is shown to
outperform classical state-of-the-art solutions that work with depth images
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