منابع مشابه
Robust Keystroke Biometric Anomaly Detection
The Keystroke Biometrics Ongoing Competition (KBOC) presented an anomaly detection challenge with a public keystroke dataset containing a large number of subjects and real-world aspects. Over 300 subjects typed case-insensitive repetitions of their first and last name, and as a result, keystroke sequences could vary in length and order depending on the usage of modifier keys. To deal with this,...
متن کاملModeling Local Video Statistics for Anomaly Detection
MODELING LOCAL VIDEO STATISTICS FOR ANOMALY DETECTION
متن کاملRobust statistics for outlier detection
When analyzing data, outlying observations cause problems because they may strongly influence the result. Robust statistics aims at detecting the outliers by searching for the model fitted by the majority of the data. We present an overview of several robust methods and outlier detection tools. We discuss robust procedures for univariate, low-dimensional, and high-dimensional data such as estim...
متن کاملDirect Robust Matrix Factorization for Anomaly Detection
Matrix factorization methods are extremely useful in many data mining tasks, yet their performances are often degraded by outliers. In this paper, we propose a novel robust matrix factorization algorithm that is insensitive to outliers. We directly formulate robust factorization as a matrix approximation problem with constraints on the rank of the matrix and the cardinality of the outlier set. ...
متن کاملRobust, Deep and Inductive Anomaly Detection
PCA is a classical statistical technique whose simplicity and maturity has seen it find widespread use as an anomaly detection technique. However, it is limited in this regard by being sensitive to gross perturbations of the input, and by seeking a linear subspace that captures normal behaviour. The first issue has been dealt with by robust PCA, a variant of PCA that explicitly allows for some ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: WIREs Data Mining and Knowledge Discovery
سال: 2017
ISSN: 1942-4787,1942-4795
DOI: 10.1002/widm.1236