3,758 research outputs found
Improving Sparse Representation-Based Classification Using Local Principal Component Analysis
Sparse representation-based classification (SRC), proposed by Wright et al.,
seeks the sparsest decomposition of a test sample over the dictionary of
training samples, with classification to the most-contributing class. Because
it assumes test samples can be written as linear combinations of their
same-class training samples, the success of SRC depends on the size and
representativeness of the training set. Our proposed classification algorithm
enlarges the training set by using local principal component analysis to
approximate the basis vectors of the tangent hyperplane of the class manifold
at each training sample. The dictionary in SRC is replaced by a local
dictionary that adapts to the test sample and includes training samples and
their corresponding tangent basis vectors. We use a synthetic data set and
three face databases to demonstrate that this method can achieve higher
classification accuracy than SRC in cases of sparse sampling, nonlinear class
manifolds, and stringent dimension reduction.Comment: Published in "Computational Intelligence for Pattern Recognition,"
editors Shyi-Ming Chen and Witold Pedrycz. The original publication is
available at http://www.springerlink.co
Logistic Regression Based on Statistical Learning Model with Linearized Kernel for Classification
In this paper, we propose a logistic regression classification method based on the integration of a statistical learning model with linearized kernel pre-processing. The single Gaussian kernel and fusion of Gaussian and cosine kernels are adopted for linearized kernel pre-processing respectively. The adopted statistical learning models are the generalized linear model and the generalized additive model. Using a generalized linear model, the elastic net regularization is adopted to explore the grouping effect of the linearized kernel feature space. Using a generalized additive model, an overlap group-lasso penalty is used to fit the sparse generalized additive functions within the linearized kernel feature space. Experiment results on the Extended Yale-B face database and AR face database demonstrate the effectiveness of the proposed method. The improved solution is also efficiently obtained using our method on the classification of spectra data
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
Facial Emotion Recognition with Sparse Coding Descriptor
With the Corona Virus Disease 2019 (COVID-19) global pandemic ravaging the world, all sectors of life were affected including education. This led to many schools taking distance learning through the use of computer as a safer option. Facial emotion means a lot to teacher’s assessment of his performance and relation to his students. Researchers has been working on improving the face monitoring and human machine interface. In this paper we presented different types of face recognition methods which include: Principal component analysis (PCA); Speeded Up Robust Features (SURF); Local binary pattern (LBP); Gray-Level Co-occurrence Matrix (GLCM) and also the group sparse coding (GSC) and come up with the fusion of LBP, PCA, SURF GLCM with GSC. Linear Kernel Support Vector Machine (LSVM) Classifier out-performed Polynomial, RBF and Sigmoid kernels SVM in the emotion classification. Results obtained from experiments indicated that, the new fusion method is capable of differentiating different types of face emotions with higher accuracy compare with the state-of-the-art methods currently available
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