A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
Filters
Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition
2013
IEEE Transactions on Neural Networks and Learning Systems
Preserved Fulltext
Based on the divide and conquer strategy, TSR decomposes the procedure of robust face recognition into outlier detection stage and recognition stage. ...
This paper proposes a novel nonnegative sparse representation approach, called two-stage sparse representation (TSR), for robust face recognition on a large-scale database. ...
[32] adopted robust stochastic approximation for online nonnegative factorization. ...
doi:10.1109/tnnls.2012.2226471
pmid:24808205
fatcat:l4zt22t6nbfwdbsb4npa6j7ln4
Robust Nonnegative Matrix Factorization via Half-Quadratic Minimization
2012
2012 IEEE 12th International Conference on Data Mining
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Nonnegative matrix factorization (NMF) is a popular technique for learning parts-based representation and data clustering. ...
In this paper, we propose a robust NMF method based on the correntropy induced metric, which is much more insensitive to outliers. ...
ROBUST NONNEGATIVE MATRIX FACTORIZATION In this section, we will derive three robust NMFs, which use the Correntropy Induced Metric or the Huber Mestimator to measure the quality of matrix approximation ...
doi:10.1109/icdm.2012.39
dblp:conf/icdm/DuLS12
fatcat:ojlkd6vy4zdmphihi7whbslf2u
Robust and Low-Rank Representation for Fast Face Identification With Occlusions
2017
IEEE Transactions on Image Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2016 pre-print arXiv:1605.02266v1 fatcat:dzekptp2rzbxvlnqearsw7k6daOther Versions
Our approach utilizes a robust representation based on two characteristics in order to model contiguous errors (e.g., block occlusion) effectively. ...
In this paper we propose an iterative method to address the face identification problem with block occlusions. ...
In correntropy-based sparse representation (CESR) [12] and structured sparse error coding (SSEC) [15] , ϑ(a) was chosen to be the indicator function of the nonnegative orthant R n + , such that a nonnegative ...
doi:10.1109/tip.2017.2675206
pmid:28252401
fatcat:d4hycbprrvf7rl4th5hmw6rkz4
Multi-model robust error correction for face recognition
2016
2016 IEEE International Conference on Image Processing (ICIP)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
Finally, the multi-model residual representation offers useful insights into understanding how different noise types affect face recognition rates. ...
In this work we present a general framework for robust error estimation in face recognition. ...
In correntropy-based sparse representation (CESR) [4] , ϑ(a) was chosen to be the indicator function of the nonnegative orthant R n + , such that a nonnegative a ≥ 0 regularization term was enforced. ...
doi:10.1109/icip.2016.7532956
dblp:conf/icip/IliadisSBWK16
fatcat:hg5adzzfrvhfzab7fxtlvqhrzi
Truncated Cauchy Non-negative Matrix Factorization for Robust Subspace Learning
2017
IEEE Transactions on Pattern Analysis and Machine Intelligence
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Non-negative matrix factorization (NMF) minimizes the Euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is ...
In this paper, we propose a Truncated Cauchy nonnegative matrix factorization (Truncated CauchyNMF) model to learn a subspace on a dataset contaminated by large magnitude noise or corruption. ...
Correntropy Induced Metric Based NMF The most closely-related work is the half-quadratic algorithm for optimizing robust NMF, which includes the Correntropy-Induced Metric (CIM)-based NMF (CIM-NMF) and ...
doi:10.1109/tpami.2017.2777841
pmid:29990056
fatcat:v7ornfymyfcvbdvaanrjpk3c6e
2021 Index IEEE Transactions on Image Processing Vol. 30
2021
IEEE Transactions on Image Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Wang, C., +, TIP 2021 7980-7994 Blind Decomposition of Multispectral Document Images Using Orthogonal Nonnegative Matrix Factorization. ...
Huang, Y., +, TIP 2021 2325-2339 Document image processing Blind Decomposition of Multispectral Document Images Using Orthogonal Nonnegative Matrix Factorization. ...
doi:10.1109/tip.2022.3142569
fatcat:z26yhwuecbgrnb2czhwjlf73qu
Robust Learning Based on The Information Theoretic Learning
2019
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Since the correntropy and generalized correntropy are both based [...] ...
However, the MCC-based loss function uses the second order measurement to constraint the representation error, which is not always the best choice. ...
In regression analysis based face recognition methods, we use training images to represent a test image. Ideally, the error image is a zero matrix. ...
doi:10.25904/1912/3265
fatcat:lqifh6vpb5ch5dw3laibal32dy
ICWMC 2017 Committee ICWMC Steering Committee ICWMC 2017 Technical Program Committee
unpublished
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
We are convinced that the participants found the event useful and communications very open. ...
The most widely used kernel in correntropy is the complex Gaussian kernel which is given by κ σ (ζ) = 1 √ 2πσ exp −|ζ| 2 2σ 2 (5) Comparing correntropy with MSE, we note that Correntropy is a local similarity ...
As we can see, the type of car is another factor used to evaluate the speed. ...
fatcat:hctrnh5xeng6tibh6stck62uui