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23
LeastSquares Probabilistic Classifier: A Computationally Efficient Alternative to Kernel Logistic Regression
 In Proceedings of International Workshop on Statistical Machine Learning for Speech Processing (IWSML2012), Kyoto
"... Human activity recognition from accelerometric data (e.g., obtained by smart phones) is gathering a great deal of attention since it can be used for various purposes such as remote healthcare. However, since collecting labeled data is bothersome for new users, it is desirable to utilize data obtain ..."
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Cited by 13 (7 self)
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Human activity recognition from accelerometric data (e.g., obtained by smart phones) is gathering a great deal of attention since it can be used for various purposes such as remote healthcare. However, since collecting labeled data is bothersome for new users, it is desirable to utilize data obtained from existing users. In this paper, we formulate this adaptation problem as learning under covariate shift, and propose a computationally efficient probabilistic classification method based on adaptive importance sampling. The usefulness of the proposed method is demonstrated in realworld human activity recognition. 1
LeastSquares TwoSample Test
 NEURAL NETWORKS, VOL.24, NO.7, PP.735–751
, 2011
"... The goal of the twosample test (a.k.a. the homogeneity test) is, given two sets of samples, to judge whether the probability distributions behind the samples are the same or not. In this paper, we propose a novel nonparametric method of twosample test based on a leastsquares density ratio estima ..."
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Cited by 11 (8 self)
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The goal of the twosample test (a.k.a. the homogeneity test) is, given two sets of samples, to judge whether the probability distributions behind the samples are the same or not. In this paper, we propose a novel nonparametric method of twosample test based on a leastsquares density ratio estimator. Through various experiments, we show that the proposed method overall produces smaller typeII error (i.e., the probability of judging the two distributions to be the same when they are actually different) than a stateoftheart method, with slightly larger typeI error (i.e., the probability of judging the two distributions to be different when they are actually the same).
Direct Divergence Approximation between Probability Distributions and Its Applications in Machine Learning
"... Approximating a divergence between two probability distributions from their samples is a fundamental challenge in statistics, information theory, and machine learning. A divergence approximator can be used for various purposes such as twosample homogeneity testing, changepoint detection, and class ..."
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Cited by 7 (7 self)
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Approximating a divergence between two probability distributions from their samples is a fundamental challenge in statistics, information theory, and machine learning. A divergence approximator can be used for various purposes such as twosample homogeneity testing, changepoint detection, and classbalance estimation. Furthermore, an approximator of a divergence between the joint distribution and the product of marginals can be used for independence testing, which has a wide range of applications including feature selection and extraction, clustering, object matching, independent component analysis, and causal direction estimation. In this paper, we review recent advances in divergence approximation. Our emphasis is that directly approximating the divergence without estimating probability distributions is more sensible than a naive twostep approach of first estimating probability distributions and then approximating the divergence. Furthermore, despite the overwhelming popularity of the KullbackLeibler divergence as a divergence measure, we argue that alternatives such as the Pearson divergence, the relative Pearson divergence, and the L2distance are more useful in practice because of their computationally efficient approximability, high numerical stability, and superior robustness against outliers.
Lighting condition adaptation for perceived age estimation
 IEICE Transactions on Information and Systems
, 2011
"... Over the recent years, a great deal of effort has been made to age estimation from face images. It has been reported that age can be accurately estimated under controlled environment such as frontal faces, no expression, and static lighting conditions. However, it is not straightforward to achieve ..."
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Cited by 7 (5 self)
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Over the recent years, a great deal of effort has been made to age estimation from face images. It has been reported that age can be accurately estimated under controlled environment such as frontal faces, no expression, and static lighting conditions. However, it is not straightforward to achieve the same accuracy level in realworld environment because of considerable variations in camera settings, facial poses, and illumination conditions. In this paper, we apply a recentlyproposed machine learning technique called covariate shift adaptation to alleviating lighting condition change between laboratory and practical environment. Through realworld age estimation experiments, we demonstrate the usefulness of our proposed method. Keywords face recognition, age estimation, covariate shift adaptation, lighting condition change, KullbackLeibler importance estimation procedure, importanceweighted regularized leastsquares 1
GPUAccelerated Feature Selection for Outlier Detection using the Local Kernel Density Ratio
"... Abstract—Effective outlier detection requires the data to be described by features that capture the behavior of normal data while emphasizing those characteristics of outliers which make them different than normal data. In this work, we present a novel nonparametric evaluation criterion for filter ..."
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Cited by 2 (0 self)
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Abstract—Effective outlier detection requires the data to be described by features that capture the behavior of normal data while emphasizing those characteristics of outliers which make them different than normal data. In this work, we present a novel nonparametric evaluation criterion for filterbased feature selection which caters to outlier detection problems. The proposed method seeks the subset of features that represent the inherent characteristics of the normal dataset while forcing outliers to stand out, making them more easily distinguished by outlier detection algorithms. Experimental results on real datasets show the advantage of our feature selection algorithm compared to popular and stateoftheart methods. We also show that the proposed algorithm is able to overcome the small sample space problem and perform well on highly imbalanced datasets. Furthermore, due to the highly parallelizable nature of the feature selection, we implement the algorithm on a graphics processing unit (GPU) to gain significant speedup over the serial version. The benefits of the GPU implementation are twofold, as its performance scales very well in terms of the number of features, as well as the number of data points.
Covariate Shift Adaptation, ClassBalance Change Adaptation, and Change Detection
"... In standard supervised learning algorithms training and test data are assumed to follow the same probability distribution. However, because of a sample selection bias or nonstationarity of the environment, this important assumption is often violated in practice, which causes a signicant estimatio ..."
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In standard supervised learning algorithms training and test data are assumed to follow the same probability distribution. However, because of a sample selection bias or nonstationarity of the environment, this important assumption is often violated in practice, which causes a signicant estimation bias. In this article, we review semisupervised adaptation techniques for coping with such distribution changes. We focus on two scenarios of such distribution change: the covariate shift (input distributions change but the inputoutput dependency does not change) and the classbalance change in classication (classprior probabilities change but classwise input distributions remain unchanged). We also show methods of change detection in probability distributions.
1WIREs Computational Statistics, 2013. Learning under NonStationarity: Covariate Shift and ClassBalance Change
"... One of the fundamental assumptions behind many supervised machine learning algorithms is that training and test data follow the same probability distribution. However, this important assumption is often violated in practice, for example, because of an unavoidable sample selection bias or nonstati ..."
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One of the fundamental assumptions behind many supervised machine learning algorithms is that training and test data follow the same probability distribution. However, this important assumption is often violated in practice, for example, because of an unavoidable sample selection bias or nonstationarity of the environment. Due to violation of the assumption, standard machine learning methods suffer a significant estimation bias. In this article, we consider two scenarios of such distribution change — the covariate shift where input distributions differ and classbalance change where classprior probabilities vary in classification — and review semisupervised adaptation techniques based on importance weighting.
Asian Conference on Machine Learning Local Kernel Density RatioBased Feature Selection for Outlier Detection
"... Selecting features is an important step of any machine learning task, though most of the focus has been to choose features relevant for classification and regression. In this work, we present a novel nonparametric evaluation criterion for filterbased feature selection which enhances outlier detect ..."
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Selecting features is an important step of any machine learning task, though most of the focus has been to choose features relevant for classification and regression. In this work, we present a novel nonparametric evaluation criterion for filterbased feature selection which enhances outlier detection. Our proposed method seeks the subset of features that represents the inherent characteristics of the normal dataset while forcing outliers to stand out, making them more easily distinguished by outlier detection algorithms. Experimental results on real datasets show the advantage of this feature selection algorithm compared to popular and stateoftheart methods. We also show that the proposed algorithm is able to overcome the small sample space problem and perform well on highly imbalanced datasets.
Detecting Interesting Events using Unsupervised Density Ratio Estimation
"... Abstract. Generating meaningful digests of videos by extracting interesting frames remains a difficult task. In this paper, we define interesting events as unusual events which occur rarely in the entire video and we propose a novel interesting event summarization framework based on the technique of ..."
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Abstract. Generating meaningful digests of videos by extracting interesting frames remains a difficult task. In this paper, we define interesting events as unusual events which occur rarely in the entire video and we propose a novel interesting event summarization framework based on the technique of density ratio estimation recently introduced in machine learning. Our proposed framework is unsupervised and it can be applied to general video sources, including videos from moving cameras. We evaluated the proposed approach on a publicly available dataset in the context of anomalous crowd behavior and with a challenging personal video dataset. We demonstrated competitive performance both in accuracy relative to human annotation and computation time.