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With the development of information technology in education, teaching is no longer face to face. Detecting abnormal learning behavior in time is valuable for the adjustment of teaching strategies. Because of the individual differences in learning behavior, it is difficult to measure with a unified standard. Artificial Immune Systems are abnormal detecting models which are inspired by Biosystem, it...
Archetypal analysis (AA) proposed by Cutler and Breiman in estimates the principal convex hull of a data set. As such AA favors features that constitute representative 'corners' of the data, i.e. distinct aspects or archetypes. We will show that AA enjoys the interpretability of clustering - without being limited to hard assignment and the uniqueness of SVD - without being limited to orthogonal representations...
Inference of latent variables from complicated data is one important problem in data mining. The high dimensionality and high complexity of real world data often make accurate inference difficult. We approach this challenge with a neural architecture we call Conjoined Twins, which is a two-layer feed forward network with a Self-Organizing Map (SOM) as its hidden layer. Its output layer can preferentially...
Most traditional face recognition systems attempt to achieve a low recognition error rate, implicitly assuming that the losses of all misclassifications are the same. In this paper, we argue that this is far from a reasonable setting because, in almost all application scenarios of face recognition, different kinds of mistakes will lead to different losses. For example, it would be troublesome if a...
In this paper, we present an active boosting algorithm to learn the object detector. This algorithm is to find good features from a confidential map instead of brute-force searching the predefined feature set. The confidential map is computed from the importance re-sampled data. A new feature is created by the linear combination of blocks that are selected from different segmented regions. In addition,...
This paper proposes a fusion model that merges the context-aware multimodal information of JADE (Java agent development framework). The context-aware multimodal information system is developed from the multi-heterogeneous context sensing devices. This multimodal not only gathers multidimensional data that aims to recognize and analyze the collected emotion information, but also emotion manages the...
Sparse representation for machine learning has been exploited in past years. Several sparse representation based classification algorithms have been developed for some applications, for example, face recognition. In this paper, we propose an improved sparse representation based classification algorithm. Firstly, for a discriminative representation, a non-negative constraint of sparse coefficient is...
Representation learning is a fundamental challenge for feature selection and plays an important role in applications such as dimension reduction, data mining and object recognition. Traditional linear representation methods, such as principal component analysis (PCA), independent component analysis (ICA) and linear discriminate analysis (LDA), have good performance on certain applications based on...
This paper proposes a frontal staircase detection algorithm using both classical Haar-like features and a novel set of PCA-base Haar-like features. Real AdaBoost is used for training a cascaded classifier. The PCA-based Haar-like features are extremely efficient at rejecting background regions at early stages in the cascade. A specifically designed scanning scheme made the algorithm constantly time...
Manifold learning algorithms, such as ISOMAP, LLE, Laplacian Eigenmaps, LTSA and so on, are designed to map nonlinear high dimensional data into the low dimensional space. The key of their success is to select a suitable neighborhood parameter. However, it is difficult to determine a proper neighborhood size for most of manifold learning methods, in particular for non-uniform data sets. An adaptive...
There is a growing interest in subspace discriminative feature extraction techniques based on tensor (multilinear) representation, which encodes an image object as a general tensor of second or even higher order. However, on one hand the computational convergence of its iterative algorithms is not guaranteed, on the other these methods are impractical for real-time applications for large training...
The classical algorithm ISOMAP can find the intrinsic low-dimensional structures hidden in high-dimensional data uniformly distributed on or around a single manifold, but if the data are sampled from multi-class, each of which corresponds to an independent manifold, and clusters formed by data points belonging to each class are separated away, several disconnected neighborhood graphs will form, which...
In this paper, we propose to kernelize linear learning machines, e.g., PCA and LDA, in the empirical kernel feature space, a finite-dimensional embedding space, in which the distances of the data in the kernel feature space are preserved. The empirical kernel feature space provides a unified framework for the kernelization of all kinds of linear machines: performing a linear machine in the finite-dimensional...
Recent development in the field of face detection highlights the benefits from large scale training samples, which can be cheaply collected through Internet. However, these large training sets are usually constructed in a rather arbitrary manner. In this paper, we empirically investigate the fundamental question of how the training set effects the performance of a given state of the art face detector...
Dimensionality reduction has been demonstrated to be an effective way for feature extraction in the pattern recognition task. In this paper, a new manifold learning algorithm, Local Discriminant Space Alignment (LDSA), is developed for nonlinear dimensionality reduction. In LDSA, the discriminant structure and the local geometry of data manifold is learned by constructing a local space for each data...
This paper develops a supervised discriminant technique, called margin maximum embedding discriminant (MMED), for dimensionality reduction of high-dimensional data. In graph embedding, our objective is to find a linear transform matrix to make the samples in the same class as compact as possible and the samples belong to the different classes as dispersed as possible. The proposed method effectively...
The high number of features in many machine vision applications has a major impact on the performance of machine learning algorithms. Feature selection (FS) is an avenue to dimensionality reduction. Evolutionary search techniques have been very promising in finding solutions in the exponentially growing search space of FS problems. This paper proposes a genetic programming (GP) approach to FS where...
Feature extraction is an important step for face recognition. The capability of feature extraction directly influences the performance of face recognition. Recently, some manifold learning algorithms have drawn much attention. Among them, neighborhood preserving projections is one of the most promising feature extraction techniques. Though NPP has been applied in many fields, it has limitations to...
In this paper, we present the manifold elastic net (MEN) for sparse variable selection. MEN combines merits of the manifold regularization and the elastic net regularization, so it considers both the nonlinear manifold structure of a dataset and the sparse property of the redundant data representation. Face based gender recognition has received much attention in the psychophysical and video surveillance...
In this paper, we proposed a new facial landmark-detection system using as edge energy function. The facial landmark-detection system is divided into a learning stage and a detection stage. The learning stage creates an interest-region model, to set up a search region of each landmark, as pre-information necessary for a detection stage and creates a detector for each landmark to detect a landmark...
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