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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,...
Domain adaptation (DA) algorithms utilize a label-rich old dataset (domain) to build a machine learning model (classification, detection etc.) in a label-scarce new dataset with different data distribution. Recent approaches transform cross-domain data into a shared subspace by minimizing the shift between their marginal distributions. In this paper, we propose a novel iterative method to learn a...
Data-driven solutions to Electric Vehicle (EV) range estimation is attracting attention recently due to the prevalence of Internet of Things (IoT). However, there raise the Big Data problems with the increased volume and number of sensory sources of unstructured data collected from the EV equipped with In-Vehicle Networks. This means that traditional statistical analysis and Machine Learning tools...
Proportion-SVM has been deeply studied due to its broad application prospects, such as modeling voting behaviors and spam filtering. However, the geometric information has been widely ignored. Thus, current methods usually show sensitivity to noises. To address these problems, in this paper, we combine the proportion learning framework with Laplacian term. We exploit the advantages of Laplacian term...
Data can encapsulate different object groupings in subspaces of arbitrary dimension and orientation. Finding such subspaces and the groupings within them is the goal of generalized subspace clustering. In this work we present a generalized subspace clustering technique capable of finding multiple non-redundant clusterings in arbitrarily-oriented subspaces. We use Independent Subspace Analysis (ISA)...
The combination of observed data and dynamical models of mean-field type over networked systems is a challenging problem because of non-linearity, high dimensionality and partial observations. In many networked systems, the effective extraction and utilization of the information contained in observed data should be accomplished by exploiting the availability of accurate predictive, proactive tools...
We live in the era of big data with dataset sizes growing steadily over the past decades. In addition, obtaining expert labels for all the instances is time-consuming and in many cases may not even be possible. This necessitates the development of advanced semi-supervised models that can learn from both labeled and unlabeled data points and also scale at worst linearly with the number of examples...
Online real-estate information systems such as Zillow and Trulia have gained increasing popularity in recent years. One important feature offered by these systems is the online home price estimate through automated data-intensive computation based on housing information and comparative market value analysis. State-of-the-art approaches model house prices as a combination of a latent land desirability...
most cancers at early stages show no obvious symptoms and curative treatment is not an option any more when cancer is diagnosed. Therefore, making accurate predictions for the risk of early cancer has become urgently necessary in the field of medicine. In this paper, our purpose is to fully utilize real-world routine physical examination data to analyze the most discriminative features of cancer based...
This paper presents a semi-parametric algorithm for online learning of a robot inverse dynamics model. It combines the strength of the parametric and non-parametric modeling. The former exploits the rigid body dynamics equation, while the latter exploits a suitable kernel function. We provide an extensive comparison with other methods from the literature using real data from the iCub humanoid robot...
This work presents a dimensionality reduction (DR) framework that enables users to perform either the selection or mixture of DR methods by means of an interactive model, here named Geo-Desic approach. Such a model consists of linear combination of kernel-based representations of DR methods, wherein the corresponding coefficients are related to coordinated latitude and longitude inside of the world...
In business, consumers interest, behavior, product profits are the insights required to predict the future of business with the current data or historical data. These insights can be generated with the statistical techniques for the purpose of forecasting. The statistical techniques can be evaluated for the predictive model based on the requirements of the data. The prediction and forecasting are...
In this paper, we propose an efficient and robust gross outlier removal method, called the Conceptual Space based Gross Outlier Removal (CSGOR) method, to remove gross outliers for geometric model fitting. In the proposed method, each data point is mapped to a conceptual space by computing the preference of "good" model hypotheses. In the conceptual space, the distributions of inliers and...
In this article, the grey relational analysis method was used to identify the key constituents of Yinhuang granules according to the anti-respiratory syncytial virus activities in drug serum by in vitro laboratory experiments. Furthermore, a model that characterizes the relationship between constituents and median effective concentrations was established through the least squares support vector machine...
Regrasping is the process of adjusting the position and orientation of an object in one's hand. The study of robotic regrasping has generally been limited to use of theoretical analytical models and cases with little uncertainty. Analytical models and simulations have so far proven unable to capture the complexity of the real world. Empirical statistical models are more promising, but collecting good...
Jensen-Shannon divergence (JSD) does not provide adequate separation when the difference between input distributions is subtle. A recently introduced technique, Chisini Jensen Shannon Divergence (CJSD), increases JSD's ability to discriminate between probability distributions by reformulating with operators from Chisini mean. As a consequence, CJSDs also carry additional properties concerning robustness...
With the advent of large numbers of data and a large number of samples, the traditional support vector machine algorithm is not applicable because of it's too much memory overhead and time overhead. Traditional parallel SVM based on MapReduce is to separate the train data into multiple sub-training sets on MapReduce-based model, these sub-datasets are trained by SVM, and then, get the support vectors...
Twitter is one of the most well known social media in this era. Each day, many of this tweets are tweeted by people. In this experiments, Indonesian tweet will be extracted as set of features and will be categorized using machine learning. This experiment will conduct research to get the best configuration to categorize Indonesian tweet. To get the best configuration, this experiment will use lexical...
This paper present a nonlinear system identification based kernel methods, such as regularization networks, support vector regression and kernel principal component analysis. In this case, black-box models are used in a particular space named reproducing kernel Hilbert space (RKHS) which only considered the input/output signals of the nonlinear system. In this particular space, the model is a linear...
Remaining useful life (RUL) prognostics is a core problem in prognostics and health management (PHM). Accurate RUL prediction is crucial not only to the verification of mission goals but also to failure prevention and maintenance decision in a more effective and efficient manner. However, the substantial nonlinearity is one of most important challenges in deterioration modeling and RUL estimation...
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