All Videos Tagged estimation (Data Science Central) - Data Science Central 2021-03-01T04:35:08Z https://www.datasciencecentral.com/video/video/listTagged?tag=estimation&rss=yes&xn_auth=no ODSC APAC 2020: Non-Parametric PDF estimation for advanced Anomaly Detection tag:www.datasciencecentral.com,2020-12-13:6448529:Video:1004959 2020-12-13T05:26:56.850Z Kuldeep Jiwani https://www.datasciencecentral.com/profile/KuldeepJiwani <a href="https://www.datasciencecentral.com/video/odsc-apac-2020-non-parametric-pdf-estimation-for-advanced-anomaly"><br /> <img alt="Thumbnail" height="134" src="https://storage.ning.com/topology/rest/1.0/file/get/8281307697?profile=RESIZE_710x&amp;ss=00%3A00%3A01.000&amp;width=240&amp;height=134" width="240"></img><br /> </a> <br></br>Anomaly Detection have been one of most sought after analytical solutions for businesses operating in the domain of Network Operation, Service Operation, Manufacturing etc. and many other sectors where continuity of operations is essential. Any degradation in operational service or an outage, implies high losses and possible customer… <a href="https://www.datasciencecentral.com/video/odsc-apac-2020-non-parametric-pdf-estimation-for-advanced-anomaly"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/8281307697?profile=RESIZE_710x&amp;ss=00%3A00%3A01.000&amp;width=240&amp;height=134" width="240" height="134" alt="Thumbnail" /><br /> </a><br />Anomaly Detection have been one of most sought after analytical solutions for businesses operating in the domain of Network Operation, Service Operation, Manufacturing etc. and many other sectors where continuity of operations is essential. Any degradation in operational service or an outage, implies high losses and possible customer churn. The data in such real world applications is generally noisy, have complex patterns and often correlated.<br /> <br /> There are techniques like Auto-Encoders available for modelling complex patterns, but they can't explain the cause in original feature space. The traditional univariate anomaly detection techniques uses the z-score and p-value methods. These rely upon unimodality and choice of correct parametric form. If assumptions are not satisfied then there would be a high number of False-Positives and False-Negatives.<br /> <br /> This is where the need for estimating a PDF (Probability Density Function) arises that too without assuming a prior parametric form i.e. Non-Parametric approach. The PDF needs to be modelled as close to the true distribution as possible. That is it should have a low bias and low variance to avoid over-smoothing and under-smoothing. Only then we would have better chances of identifying true anomalies.<br /> <br /> Approaches like KDE - Kernel Density Estimation assist in such non-parametric estimations. As per research the type of kernel has a lesser role to play than the bandwidth for a good PDF estimation. The default bandwidth selection technique used in both Python and R packages over-smooths the PDF and is not suitable for Anomaly Detection.<br /> <br /> We will explain another method, where we run optimisation over a cost function based on modelling Gaussian kernel via FFT (Fast Fourier Transform), to obtain the appropriate bandwidth. Then we will show how we can apply it for Anomaly Detection even when the data is multi-modal (have multiple peaks) and the distribution can be of any shape.<br /> <br /> Based on research paper "Optimal Kernel Density Estimation using FFT based cost function", for ICDM 2020, New York ICDM 2020: Optimal Kernel Density Estimation using FFT based cost function tag:www.datasciencecentral.com,2020-08-22:6448529:Video:978205 2020-08-22T06:07:42.700Z Kuldeep Jiwani https://www.datasciencecentral.com/profile/KuldeepJiwani <a href="https://www.datasciencecentral.com/video/icdm-2020-optimal-kernel-density-estimation-using-fft-based-cost"><br /> <img alt="Thumbnail" height="150" src="https://storage.ning.com/topology/rest/1.0/file/get/7563938465?profile=RESIZE_710x&amp;ss=00%3A00%3A01.000&amp;width=240&amp;height=150" width="240"></img><br /> </a> <br></br>The full research paper is available in the journal: <a href="https://lnkd.in/ghCZFMp">https://lnkd.in/ghCZFMp</a><br></br> <br></br> Abstract: Kernel density estimation (KDE) is an important method in nonparametric learning, but it is highly sensitive to the bandwidth parameter. The existing techniques tend to under smooth or over smooth the… <a href="https://www.datasciencecentral.com/video/icdm-2020-optimal-kernel-density-estimation-using-fft-based-cost"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/7563938465?profile=RESIZE_710x&amp;ss=00%3A00%3A01.000&amp;width=240&amp;height=150" width="240" height="150" alt="Thumbnail" /><br /> </a><br />The full research paper is available in the journal: <a href="https://lnkd.in/ghCZFMp">https://lnkd.in/ghCZFMp</a><br /> <br /> Abstract: Kernel density estimation (KDE) is an important method in nonparametric learning, but it is highly sensitive to the bandwidth parameter. The existing techniques tend to under smooth or over smooth the density estimation. Especially when data is noisy, which is a common trait of real-world data sources. This paper proposes a fully data driven approach to avoid under smoothness and over smoothness in density estimation. This paper uses a cost function to achieve optimal bandwidth by evaluating a weighted error metric, where the weight function ensures low bias and low variance during learning. The density estimation uses the computationally efficient Fast Fourier Transform (FFT) to estimate the univariate Gaussian kernel density. Thus bringing the computation cost of a single density evaluation from O(n2) to O(m log(m)), where m &lt;&lt; n and m being the grid points of FFT. Based upon simulation results this paper significantly outperforms the de-facto classical methods and the more recent papers over a standard benchmark dataset. The results specially shines apart from the recent and classical approaches when data contains significant noise.