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Outlier detection is the task of automatic identification of unknown data not covered by training data (e.g. a previously unknown class in classification). We explore outlier detection in the presence of hubs and anti-hubs, i.e. data objects which appear to be either very close or very far from most other data due to a problem of measuring distances in high dimensions. We compare a classic distance...
This paper proposes a new Wi-Fi based indoor positioning method that is robust over unstable Wi-Fi access points (APs). Because Wi-Fi based indoor positioning relies on unstable and uncontrollable infrastructure (Wi-Fi APs), the positioning performance significantly decreases when such unstable APs are included in the localization system. This paper proposes a indoor positioning method by employing...
In this paper we examine a class of multiple-input, single-output (MISO) nonlinear systems of the block-oriented structure. In particular, we focus on MISO Hammerstein systems being the cascade connection of a multivariate nonlinearity with a linear dynamical subsystem. In order to alleviate an apparent curse of dimensionality occurring in the problem of estimating the nonlinearity, we propose to...
In this paper we develop a semi-parametric approach to the problem of identification of multivariate Hammerstein systems. A nonlinearity in general multivariate Hammerstein systems is represented by projecting the d-dimensional input signal onto one dimensional subset which, in turn, is mapped by a univariate nonparametric function to an internal signal of the system. Such a parsimonious representation...
This paper presents a simulation-based empirical study of the performance profile of random sub sample ensembles with a hybrid mix of base learner composition in high dimensional feature spaces. The performance of hybrid random sub sample ensemble that uses a combination of C4.5, k-nearest neighbor (kNN) and naïve Bayes base learners is assessed through statistical testing in comparison to those...
In statistical pattern recognition, high dimensionality is a major cause of the practical limitations of many pattern recognition technologies. Moreover, it has been observed that a large number of features may actually degrade the performance of classifiers if the number of training samples is small relative to the number of features. This fact, which is referred to as the “peaking phenomenon”, is...
In this paper, we propose a new feature evaluation method that forms the basis for feature ranking and selection. The method starts by generating a number of feature subsets in a random fashion and evaluates features based on the derived subsets. It then proceeds in a number of stages. In each stage, it inputs the features whose ranks in the previous stage were above the median rank and re-evaluates...
This paper presents a semi-parametric approach to the problem of identification of multivariate Hammerstein systems. A nonlinearity in general multivariate Hammerstein systems is represented by projecting the d-dimensional input signal onto one dimensional subset which, in turn, is mapped by a univariate nonparametric function to an internal signal of the system. Such a parsimonious representation...
This paper presents application of machine learning ensembles, which randomly project the original high dimensional feature space onto multiple lower dimensional feature subspaces, to classification problems with high-dimensional feature spaces. The motivation is to address challenges associated with algorithm scalability, data sparsity and information loss due to the so-called curse of dimensionality...
In some machine learning problems, the dataset has multiple views which may be obtained using different sensors or applying different sampling techniques. These views may have sufficient or partial information about the target concept. In this paper, a method that we called parallel interacting multiview learning (PIML) is proposed in which the views interact during the training process using the...
A critical issue in the design of morphological operators from training data is the limited amount of training images. Recently, a multilevel design approach has been proposed to improve the performance of the designed operators, without increasing the number of training images. Since the operators are usually designed using low-resolution images, this work investigates the use of multiple low resolution...
In order to solve the dimensionality curse of BP neural network in pattern recognition, this paper proposes a model of dimensionality reduction which based on rough set theory. While training network, the model first carries out attribute reduction based on rough set theory, and then picks up important characteristics of ideal samples to reduce input space dimensions. Hence the speed of network training...
In order to solve the problem of "curse of dimensionality", which means that the state spaces will grow exponentially in the number of features, in large discrete state spaces in reinforcement learning, a reinforcement learning method based on Gaussian processes is proposed. The Gaussian processes model can represent the distributions of functions, and it can be used to get a distribution...
This paper proposes a hierarchical architecture, HieNet, that utilizes the K-Iterations Fast Learning artificial Neural Network (KFLANN). Effective in its clustering capabilities, the KFLANN is capable of providing more stable and consistent clusters that are independent data presentation sequences (DPS). Leveraging on the ability to provide more consistent clusters, the KFLANN is initially used to...
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