Praneeth Vepakomma

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In this work we present A-Wristocracy, a novel framework for recognizing very fine-grained and complex inhome activities of human users (particularly elderly people) with wrist-worn device sensing. Our designed A-Wristocracy system improves upon the state-of-the-art works on in-home activity recognition using wearables. These works are mostly able to detect(More)
BACKGROUND AND OBJECTIVES Parkinson's disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity and tremors. Analyzing human gait serves to be useful in studies aiming at early recognition of the disease. In this paper we perform a comparative analysis of various nature inspired(More)
Nonlinear dimensionality reduction techniques of today are highly sensitive to outliers. Almost all of them are spectral methods and differ from each other over their treatment of the notion of neighborhood similarities computed amongst the high-dimensional input data points. These techniques aim to preserve the notion of this similarity structure in the(More)
In this paper we propose an algorithm for non-linear embedding of affinity tensors obtained by measuring higher-order similarities between high-dimensional points. We achieve this by preserving the original triadic similarities using another triadic similarity function obtained by sum of squares of diadic similarities in a low-dimension. We show that this(More)
In a regression setting we propose algorithms that reduce the dimensionality of the features while simultaneously maximizing a statistical measure of dependence known as distance correlation between the low-dimensional features and a response variable. This helps in solving the prediction problem with a low-dimensional set of features. Our setting is(More)
In our work, we propose a novel formulation for supervised dimensionality reduction based on a nonlinear dependency criterion called Statistical Distance Correlation, [Székely et al., 2007]. We propose an objective which is free of distributional assumptions on regression variables and regression model assumptions. Our proposed formulation is based on(More)
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