Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
The density peak based clustering algorithm is a recently proposed clustering approach. It uses the local density of each data and the distance to the nearest neighbor with higher density to isolate and identify the cluster centers. After the cluster centers are identified, the other data are assigned labels equaling to those of their nearest neighbors with higher density. This algorithm is simple...
The density peak based clustering algorithm is a simple yet effective clustering approach. This algorithm firstly calculates the local density of each data and the distance to the nearest neighbor with higher density. Based on the assumption that cluster centers are density peaks and they are relatively far from each other, this algorithm isolates the candidates of cluster centers from the non-center...
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...
Kernel principal component analysis (kPCA) learns nonlinear modes of variation in the data by nonlinearly mapping the data to kernel feature space and performing (linear) PCA in the associated reproducing kernel Hilbert space (RKHS). However, several widely-used Mercer kernels map data to a Hilbert sphere in RKHS. For such directional data in RKHS, linear analyses can be unnatural or suboptimal. Hence,...
Text data present in scene images may be the important clue for indexing, automatic footnote, and indexing of images. Now-a-days extraction of text from images has become one of the fastest growing research areas in the field of computer vision. In scene images, text data are present with huge variations in font sizes, styles, alignments, and orientations. These variations make the task of detection...
This work seeks to improve upon the accuracy of birdsong analysis based species recognition. We intend to accomplish this by creating a more effective bird syllable segmentation algorithms (MIRS), Support Vector machine based classifiers are used to train the features of IRS and MIRS. The experimental results show the effectiveness of the proposed algorithm.
While kernel methods using a single Gaussian kernel have proven to be very successful for nonlinear classification, in case of learning problems with a more complex underlying structure it is often desirable to use a linear combination of kernels with different widths. To address this issue, this paper presents a classification algorithm based on a jointly convex constrained optimization formulation...
Demand side management (DSM) is a key mechanism to make smart grids cost efficient using electricity price forecasting issue. Price forecasting method takes the big price data into account, and gives estimates of the future electricity price. However, most of existing price forecasting methods cannot avoid redundancy at feature selection and lack of an integrated framework that coordinates the steps...
Configuring an Evolutionary Algorithm (EA) can be a haphazard and inefficient process. An EA practitioner may have to choose between a plethora of search operator types and other parameter settings. In contrast, the goal of EA principled design is a more streamlined and systematic design methodology, which first seeks to better understand the problem domain, and only then uses such acquired insights...
Scaling-up scientific data analysis and machine learning algorithms for data-driven discovery is a grand challenge that we face today. Despite the growing need for analysis from science domains that are generating ‘Big Data’ from instruments and simulations, building high-performance analytical workflows of data-intensive algorithms have been daunting because: (i) the ‘Big Data’ hardware and software...
In this paper, we pose and address some of the unique challenges in the analysis of scientific Big Data on supercomputing platforms. Our approach identifies, implements and scales numerical kernels that are critical to the instantiation of theory-inspired analytic workflows on modern computing architectures. We present the benefits of scalable kernels towards constructing algorithms such as principal...
Kernel methods represent some of the most popular machine learning tools for data analysis. Since exact kernel methods can be prohibitively expensive for large problems, reliable low-rank matrix approximations and high-performance implementations have become indispensable for practical applications of kernel methods. In this work, we introduce spectrum-revealing Cholesky factorization, a reliable...
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...
Multi-view learning concentrates on multiple distinct feature sets mainly following either the consensus principle or the complementary principle. SVM-2K, as a typical SVM-based multi-view learning model, extends SVM for multi-view learning by combining Kernel Canonical Correlation Analysis (KCCA). However, SVM-2K cannot fully unleash the power of the complementary information among different feature...
Based on the kernel method and graph theory, this paper proposes a novel Kernel Non-negative Matrix Factorization with Local and Non-local feature (LN-KNMF) approach for face recognition. We establish the objective function in kernel space which incorporates two scatter quantities, namely local scatter and non-local scatter. They are determined by the local adjacent graph matrix and non-local adjacent...
Classification methods has become increasingly popular for biomedical and bioinformatical data analysis. However, due to the difficulty of data acquisition, sometimes we could only obtain small-scale datasets which may leads to unreasonable generalization performances. For SVM-like algorithms, we could resort to Large Margin theory to find out solutions for such dilemma. Recent studies on large margin...
We propose a simple yet effective blur kernel re-initialization method in a coarse-to-fine framework for blind image deblurring. The proposed method is motivated by observing that most deblurring algorithms use only an estimated blur kernel at the coarser level to initialize a blur kernel for the next finer level. Based on this observation, we design an objective function to exploit both a blur kernel...
This paper is concerned with the input design for Kernel-Based system identification methods. It proposes a method for input design which maximizes the information obtained through experiment based on a prior information on the target systems. The mutual information is adopted as such an information measure, and its closed form expression is obtained in terms of the kernel matrix, which expresses...
Data explosion drives data analysis tools to update faster and faster, while clustering plays an indispensable role in knowledge discovery. Whereas, most of the clustering algorithms only effect on those linear separable data. Kernel-based clustering methods perform well on data sets with non-linear inner structure, but at the same time, the requirement of large memory and running time induce poor...
In many computer vision systems, one object can be described by multi-view data. Compared with individual view, multi-view data can contain complete and complementary information of the problem. But when views capture information which is uniquely but not complete enough to give an uniform learning performance, multi-view data may degrade the learning performance and it is therefore not an ideal solution...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.