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In this paper, a dimensionality reduction method applied on facial expression recognition is investigated. An unsupervised learning framework, projective complex matrix factorization (proCMF), is introduced to project high-dimensional input facial images into a lower dimension subspace. The proCMF model is related to both the conventional projective nonnegative matrix factorization (proNMF) and the...
Physical therapy involves exercising and manipulating the body as a cost-effective treatment to improve mobility and relieve pain. In this paper, we developed a system to depend on some defined features from sample exercises and compare with the derived features from repeated exercises. The comparison results will reflect the benefit of these exercises and determine plan treatment for a patient. Experiments...
This paper presents an image representation approach which is based on matrix factorization in the complex domain and called exemplar-embed complex matrix factorization (EE-CMF). The proposed EE-CMF approach can very effectively improve the performance of facial expression recognition. Moreover, Wirtinger's calculus was employed to determine derivatives. The gradient descent method was utilized to...
Nowadays, sound event detection (SED) is a popular study in machine listening area. Detecting overlapping sound events, in which many sound events occur simultaneously, is challenging and interesting topic. Besides, non-negative matrix factorization (NMF) and its derived methods are suitably used to perform SED. This paper presents a survey of recent approaches in SED based on NMF methods that provides...
Single-channel source separation is an approach to decomposing a single-channel recording into its sources without understanding how the sources are mixed. This work develops a sparse regularized nonnegative matrix factorization scheme with spatial dispersion penalty (SpaSNMF). To preserve spatial locality structured information on the basis for sound source separation, intra-sample structure constraints...
In this paper, we introduce a new color image segmentation by using superpixels as feature representation and Manhattan Nonnegative Matrix Factorization (MahNMF) for accurate segmentation. Firstly, the image pixels are grouped into superpixels and considered as the coarse features. The next step is then conducted by factorizing the matrix feature into two nonnegative matrices, which respectively imply...
Segmentation of the whole liver region from computed tomography (CT) image is the first step in the computer-aided diagnosis for liver disease. In this paper, we propose a new method for segmenting liver region from 3D CT images of abdomen using enhanced Otsu method. Our algorithm uses Otsu method with some improvements to construct intensity model and shape model for liver. First, the 3D CT image...
This paper introduces a novel two dimensional feature extraction method for environmental sound classification, based on two dimensional semi-nonnegative matrix factorization (2D Semi-NMF) of scale-frequency maps. We first extract scale-frequency maps (SFMs) from the input signals, and this feature is considered preserving scale and frequency characteristics of signals. Second, a 2D Semi-NMF method...
Nonnegative matrix factorization (NMF) is a recent method used to decompose a given data matrix into two nonnegative sparse factors. There are many techniques applied to enhance abilities of NMF, particularly kernel technique which discovering higher-order correlation between data points and obtaining more powerful latent features. This paper presents an overview of kernel methods on NMF along with...
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