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Transfer learning has achieved promising results by leveraging knowledge from the source domain to annotate the target domain which has few or none labels. Existing methods often seek to minimize the distribution divergence between domains, such as the marginal distribution, the conditional distribution or both. However, these two distances are often treated equally in existing algorithms, which will...
We address two important issues in causal discovery from nonstationary or heterogeneous data, where parameters associated with a causal structure may change over time or across data sets. First, we investigate how to efficiently estimate the "driving force" of the nonstationarity of a causal mechanism. That is, given a causal mechanism that varies over time or across data sets and whose...
Consider a problem of estimating an unknown high dimensional density whose support lies on unknown low-dimensional data manifold. This problem arises in many data mining tasks, and the paper proposes a new geometrically motivated solution for the problem in manifold learning framework, including an estimation of an unknown support of the density. Firstly, tangent bundle manifold learning problem is...
Similarity measure is a central problem in time series data mining. Although most approaches to this problem have been developed, with the rapid growth of the amount of data, we believe there is a challenging demand for supporting similarity measure in a fast and accurate way. In this paper, we propose a new time series representation model and a corresponding similarity measure, which is able to...
Linear Discriminant Analysis (LDA) is widely-used for supervised dimension reduction and linear classification. Classical LDA, however, suffers from the ill-posed estimation problem on data with high dimension and low sample size (HDLSS). To cope with this problem, in this paper, we propose an Adaptive Wishart Discriminant Analysis (AWDA) for classification, that makes predictions in an ensemble way...
Density peak (DP) based clustering algorithm is a recently proposed clustering approach and has been shown to be with great potential. This algorithm is based on the simple assumption that cluster centers have high local density and they are relatively far from each other. This observation is used to isolate cluster centers from other data. By making use of the density relationship among neighboring...
How to reduce the computation time and how to improve the quality of the clustering result are the two major research issues. Although several efficient and effective clustering algorithms have been presented, none of which is perfect. As such, an effective clustering algorithm, which is based on the prediction of searching information to determine the search directions at later iterations and employs...
In accordance with the problem that it is difficult for radar simulator to load massive broadband frequency sweep data, this paper proposes an approach for extracting the wideband radar targets' scattering center parameters to reduce the storage requirement of frequency sweep data and solve the efficiency and precision problem of loading data. Firstly, resampling is conducted to echo signal in order...
Most modern search engines feature keyword based search interfaces. These interfaces are usually found on websites belonging to enterprises or governments or sites related to news articles, blogs and social media that contain a large corpus of documents. These collections of documents are not easily indexed by web search engines, and are considered as hidden web databases. These databases provide...
This paper describes a position estimation approach based on high-frequency voltage injections for dual three-phase permanent magnet synchronous machines (DTP-PMSM) whose sets of windings are spatially shifted by 360 electrical degrees. Fail-operational drives gain more and more importance in automotive applications. Typically, multiphase machines are used to realize fail-operational properties. Besides...
This paper aims at construction of a system which assumes food textures. The system consists of equipment for obtaining the load and the sound signals while the probe is stabbing the food, and the neural network model infers the degree of the food texture. In the experiment, the validity of our proposed system is discussed.
In this paper we present our developed and evaluated method for the dynamic mapping of the vertical characteristics inside a building. For achieving that, we extract data from smart-phone sensors and use those data for altitude estimation via the barometric formula. We introduce a novel approach for the extraction of reference pressure during the outdoor-to-indoor-transition of the user inside a building,...
Highly valued Information technology (IT) service contracts involve the delivery of complex IT services, such as migrating the client's IT infrastructure to the Cloud, Mainframes, among others. IT service providers usually compete to win these IT service contracts. In order to bid on such deals, IT service providers need to price/quote the solution that they propose to the client, trying to convince...
Pulse photopletysmographic signal (PPG) is modulated by the respiratory rate, so there are some algorithms capable to extract respiratory information from the derived PPG signals, as the Pulse Amplitude Variability (PAV). Previous works have shown that the use of the PPG leads to different results depending on the PPG sensor location (finger and forehead). Therefore, a database recording finger and...
The electric power panel is widely used in aircraft engineering. Extracting the degradation feature of power panel is very important for degradation modeling and RUL estimation. This paper employed an Ensemble Empirical Mode Decomposition (EEMD) based method to extract the degradation statistics feature of power panel. First, the degradation data is decomposed into independent Intrinsic Mode Functions...
In this paper, we present an impression estimation method for television commercials with a visualization method. Our method estimates the impressions viewers might have of a new proposal for a TV commercial written in text as weighted favorable factors and visualizes the estimated favorable factors. During the production of TV commercials, it is important to create commercials that clearly communicate...
Vital sign (e.g., breathing rate) monitoring has become increasingly more important because it can offer useful clues to medical conditions such as sleep disorders or anomalies. There is a compelling need for technologies that enable contact-free, easy deployment, and long-term vital sign monitoring for healthcare. In this paper, we present a SonarBeat system to leverage a phase based active sonar...
Morphological attribute profiles (MAPs) are one of the most effective methodologies to characterize the spatial information in remote sensing images. This technique extracts components able to accurately describe objects in the surface of the Earth. In this work, we present a new method for impervious surface extraction from multispectral images using morphological attribute profiles. The proposed...
In this paper, we propose a multiple line extraction method from multimodal data points in high dimensional space. It can sparsely represent multimodal sensor network data by utilizing high correlation among channels in the data. We exploit the idea of Color Lines, which is a model using high correlation among RGB channels in computer vision. It represents real color images as a collection of multiple...
In general, dynamic systems are systems with time-dependent behavior. Dynamic systems are characterized by the non-stationary data sequences they emit. One particular way to model these non-stationary sequences is to consider them as a sequence of stationary segments, regimes, where each regime is separated by regime switching points from both the preceding and subsequent regimes. In system identification...
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