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PCANet is a simple network using Principal Component Analysis (PCA) for image classification and obtained high accuracies on a variety of datasets. PCA projects explanatory variables on a subspace that the first component has the largest variance. On the other hand, Partial Least Squares (PLS) regression projects explanatory variables on a subspace that the first component has the largest covariance...
Background Estimation in video consists in extracting a foreground-free image from a set of training frames. In this paper, we overview a temporal-spatial block-level approach for background estimation in video and present their results in the SBMnet dataset. First, the employed approach uses a Temporal Analysis module to obtain a compact representation of the training data that is later clustered...
Nonlinear multilayer principal component analysis (NMPCA) is well-known as an improved version of principal component analysis (PCA) using a five layer bottleneck neural network. NMPCA enables us to extract nonlinear hidden structure from high dimensional data, however, it has been difficult for users to understand obtained results, because trained results of NMPCA have many different locally optimal...
Two techniques to further enhance the efficiency of Evolutionary Algorithms (EAs), even those which have already been accelerated by implementing surrogate evaluation models or metamodels to overcome a great amount of costly evaluations, are presented. Both rely upon the use of a Kernel Principal Component Analysis (Kernel PCA or KPCA) of the design space, as this reflects upon the offspring population...
Face recognition has been receiving continuous academic and commercial attention for the last decades. In this paper, we construct two face recognition systems adopting SVM and Adaboost as the classifiers with fast PCA for facial feature representation. The detailed discussions about algorithm realization are given. Comparison between the two systems and analysis of them are provided through several...
Outlier detection or anomaly detection is an important and challenging issue in data mining, even so in the domain of energy data mining where data are often collected in large amounts but with little labeled information. This paper presents a couple of online outlier detection algorithms based on principal component analysis. Novel algorithmic treatments are introduced to build incremental PCA and...
Many security techniques working at the physical layer need a correct channel state information (CSI) at the transmitter, especially when devices are equipped with multiple antennas. Therefore such techniques are vulnerable to pilot contamination attacks (PCAs) by which an attacker aims at inducing false CSI. In this paper we provide a solution to some PCA methods, by letting two legitimate parties...
A principal components analysis (PCA) algorithm is one of the most important algorithms that has been used for doing many tasks; for example, data dimension reduction, data compression such as image compression, pattern recognition such as face detection and recognition, and many other things. An improved principal components analysis (IPCA) algorithm is similar to the PCA algorithm except that it...
Physical measurement have been becoming increasingly helpful in monitoring the humans health status. Manual measurement of physical status is time consuming and may result in misdiagnosing, so an automatic method for identification the status of physical is urgently needed. This paper presents a novel feature extraction method based on using constrained high dispersal network for depth images and...
A method is proposed to distinguish patients with schizophrenia from healthy controls based on data measured by functional near-infrared spectroscopy (fNIRS) during a cognitive task, which combines principal component analysis (PCA) and support vector machine (SVM). Firstly, a data reduction technique is applied prior to PCA, and then PCA is used to extract features on oxygenated hemoglobin (oxy-Hb)...
We present a novel feature descriptor for 3D human action recognition using graph signal processing techniques. A linear subspace is learned using graph total variation and graph Tikhonov regularizers, transforming 3D time derivative information into a representation that is robust against noisy skeleton measurements. The graph total variation regularizer learns an action representation that encourages...
Gait recognition with a single sample per person (SSPP) is a challenging problem but has so far drawn little research attention. Inspired by similar research in face recognition, we propose to utilize the intra-class variation information learned from an additional generic training set with multiple samples per person to improve the representation of the query sample. We learn a sparse variation dictionary...
Given several related tasks, multi-task learning can improve the performance of each task through sharing parameters or feature representations. In this paper, we apply multi-task learning to a particular case of distance metric learning, in which we have a small amount of labeled data. Consider the effectiveness of semi-supervised learning handling few labeled machine learning problems, we integrate...
In this paper, we propose a human gesture recognition algorithm using impulse radio ultra-wideband (IR-UWB) radar. The radar signal is transmitted into a three dimensional space, however, the received signal is only expressed in one dimensional. Therefore, it is difficult to classify 3-D gestures by analyzing specific features, such as power, peak value, index of peak value, and other values of received...
In this paper, a new object recognition framework is presented. The framework includes a variety of object recognition approaches based on Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and the K-nearest neighbor (K-NN). A color image vector representation model is also introduced. Based on the representation model, color Eigenspace is constructed using PCA and LDA for feature...
Learning distributed word representations (word embeddings) has gained much popularity recently. Current learning approaches usually treat all dimensions of the embeddings as homogeneous, which leads to non-structured representations where the dimensions are neither interpretable nor comparable. This paper proposes a method to generate ordered word embed-dings where the significance of the dimensions...
In case when higher-order statistic is used for local feature aggregation, final descriptor can have very high dimensionality. In this paper different methods for descriptor dimensionality reduction are evaluated for land-use classification. Concretely, aerial image classification accuracy is compared for the cases when dimensionality reduction is made per band with fixed and variable sizes. For both...
This paper proposed a new Vehicle Make Recognition (VMR) method using the PCANet features extracted from vehicle front view images. The PCANet architecture processes every input vehicle image through only three very simple data processing components: cascaded principle component analysis (PCA), binary hashing, and block-wise histograms, and generates a sparse vector as the feature representation....
Microcalcifications are tiny calcium accumulations in the breast and are a warning sign of possible breast carcinoma in the early stages of its formation, so it is highly important for radiologists to identify them in a digital mammography and also to be able to discern in which category of BIRADS they belong. In this study the focus is to classify into BIRADS 2, 3 and 4, categories in which the advance...
In recent years, the IoT application and the biometric-based authorization become popular. This paper proposes a face recognition system with high accuracy rate based on extended Local Binary Pattern, and applies it as an access control system on an IoT device which is always low-cost, low-power and small-footprint. The proposed face recognition system includes three parts, face detection, feature...
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