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Indoor localization technology based on the received signal strength (RSS) of wireless access point (AP) has become very popular in recent years. Considering that the Wi-Fi signal is unstable and uncertain, and the contribution of different APs to the localization are different, a fuzzy indoor localization algorithm based on dynamic weights of APs is proposed in this paper. The Multidimensional-Scaling...
In clustering applications, multiple views of the data are often available. Although clustering could be done within each view independently, exploiting information across views is promising to gain clustering accuracy improvement. A common assumption in the field of multi-view learning is that the clustering results from multiple views should be consistent with a latent clustering. However, the potential...
Active Constraint Learning (ACL) is continuously gaining popularity in the area of constrained clustering due to its ability to achieve performance gains via incorporating minimal feedback from a human annotator for selected instances. For constrained clustering algorithms, such instances are integrated in the form of Must-Link (ML) and Cannot-Link (CL) constraints. Existing iterative uncertainty...
Clustering often benefits from side information. In this paper, we consider the problem of multi-way constrained spectral clustering with pairwise constraints which encode whether two nodes belong to the same cluster or not. Due to the nontransitive property of cannot-link constraints, it is hard to incorporate cannot-link constraints into the framework. We settle this difficulty by restricting the...
Kernel-based clustering is one of the most popular methods for partitioning nonlinearly separable dataset. However, exhaustive search for the global optimum is NP-hard. Iterative procedure such as k-means can be used to seek one of the local minima. Unfortunately, it is easily trapped into degenerate local minima when the prototypes of clusters are ill-initialized. In this paper, we restate the optimization...
We introduce a new fuzzy relational clustering technique with Local Scaling Parameter Learning (LSPL). The proposed approach learns the underlying cluster dependent dissimilarity measure while finding compact clusters in the given data set. The learned measure is a Gaussian similarity function defined with respect to each cluster that allows to control the scaling of the clusters and thus, improve...
Bhattacharrya distance (BD) is a widely used distance in statistics to compare probability density functions (PDFs). It has shown strong statistical properties (in terms of Bayes error) and it relates to Fisher information. It has also practical advantages, since it strongly relates on measuring the overlap of the supports of the PDFs. Unfortunately, even with common parametric models on PDFs, few...
Millimetric Waves Images (MMW) are becoming more and more useful in the passive detection of threaten objects based on plastic substances as explosives or sharp/cutting weapons. Our goal is to achieve segmentation of the body and concealed threats dealing with the inherent problems of this type of images: noise, low resolution and intensity inhomogeneity. In this work we present the results of applying...
In this paper, the method that measuring dataset of knitted yarns is clustered using improving fuzzy kernel c-Means (FKCM) clustering algorithm is proposed. In FKCM clustering algorithm, the data of low dimension input space is mapped to high dimension feature space, FCM clustering algorithm is performed in feature space, then the constraint optimization distance matrix and membership matrix of testing...
Extracting and analyzing the interesting patterns from spatio-temporal databases, have drawn a great interest in various fields of research. Recently, a number of experiments have explored the problem of spatial or temporal data mining, and some clustering algorithms have been proposed. However, not many studies have been dealing with the integration of spatial data mining and temporal data mining...
Multiview clustering partitions a dataset into groups by simultaneously considering multiple representations (views) for the same instances. Hence, the information available in all views is exploited and this may substantially improve the clustering result obtained by using a single representation. Usually, in multiview algorithms all views are considered equally important, something that may lead...
We propose a dimension reduction technique named Resilient Subclass Discriminant Analysis (RSDA) for high dimensional classification problems. The technique iteratively estimates the subclass division by embedding the Fisher Discriminant Analysis (FDA) with Expectation-Maximization (EM) in Gaussian Mixture Models (GMM). The new method maintains the adaptability of SDA to a wide range of data distributions...
In this paper, we adopt a differential-geometry viewpoint to tackle the problem of learning a distance online. As this problem can be cast into the estimation of a fixed-rank positive semidefinite (PSD) matrix, we develop algorithms that exploits the rich geometry structure of the set of fixed-rank PSD matrices. We propose a method which separately updates the subspace of the matrix and its projection...
In the view of characteristics for coke micrograph, a segmentation algorithm combining mean shift and edge confidence, is proposed. Firstly, the edge confidence of image pixels is calculated, and with the edge confidence the weighting function of mean shift algorithm is computed, the sampling points of feature space are weighted in order to improve the accuracy of detected modes. Secondly, coke image...
Computer assisted or automated histological grading of tissue biopsies for clinical cancer care is a long-studied but challenging problem. It requires sophisticated algorithms for image segmentation, tissue architecture characterization, global texture feature extraction, and high-dimensional clustering and classification algorithms. Currently there are no automatic image-based grading systems for...
Mean shift algorithm is a statistics iterative algorithm which is widely used, its increment (namely mean shift vector) of iterative point in each iteration step changes adaptively. This paper presents an extensional mean shift vector, and proves convergence of mean shift algorithm which using the extensional mean shift vector. In addition, we did an experiment - using mean shift algorithm to solve...
In this paper, the fuzzy classification functions of the entropy regularized fuzzy c-means for data with tolerance using kernel functions are proposed. First, the standard clustering algorithm for data with tolerance using kernel functions are introduced. Second, the fuzzy classification function for fuzzy c-means without tolerance using kernel functions is discussed as the solution of a certain optimization...
Gaussian blurring mean-shift (GBMS) is a nonparametric clustering algorithm, having a single bandwidth parameter that controls the number of clusters. The algorithm iteratively shrinks the data set under the application of a mean-shift update, stops in just a few iterations and yields excellent clusterings. We propose several families of generalised GBMS (GGBMS) algorithms based on explicit, implicit...
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