Half-space mass: a maximally robust and efficient data depth method
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| 024 | 7 | 0 | |a 10.1007/s10994-015-5524-x |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-015-5524-x | ||
| 245 | 0 | 0 | |a Half-space mass: a maximally robust and efficient data depth method |h [Elektronische Daten] |c [Bo Chen, Kai Ting, Takashi Washio, Gholamreza Haffari] |
| 520 | 3 | |a Data depth is a statistical method which models data distribution in terms of center-outward ranking rather than density or linear ranking. While there are a lot of academic interests, its applications are hampered by the lack of a method which is both robust and efficient. This paper introduces Half-Space Mass which is a significantly improved version of half-space data depth. Half-Space Mass is the only data depth method which is both robust and efficient, as far as we know. We also reveal four theoretical properties of Half-Space Mass: (i) its resultant mass distribution is concave regardless of the underlying density distribution, (ii) its maximum point is unique which can be considered as median, (iii) the median is maximally robust, and (iv) its estimation extends to a higher dimensional space in which the convex hull of the dataset occupies zero volume. We demonstrate the power of Half-Space Mass through its applications in two tasks. In anomaly detection, being a maximally robust location estimator leads directly to a robust anomaly detector that yields a better detection accuracy than half-space depth; and it runs orders of magnitude faster than $$L_2$$ L 2 depth, an existing maximally robust location estimator. In clustering, the Half-Space Mass version of K-means overcomes three weaknesses of K-means. | |
| 540 | |a The Author(s), 2015 | ||
| 690 | 7 | |a Half-space mass |2 nationallicence | |
| 690 | 7 | |a Mass estimation |2 nationallicence | |
| 690 | 7 | |a Data depth |2 nationallicence | |
| 690 | 7 | |a Robustness |2 nationallicence | |
| 700 | 1 | |a Chen |D Bo |u Faculty of Information Technology, Monash University, 3168, Clayton, VIC, Australia |4 aut | |
| 700 | 1 | |a Ting |D Kai |u School of Engineering and Information Technology, Federation University Australia, 3842, Churchill, VIC, Australia |4 aut | |
| 700 | 1 | |a Washio |D Takashi |u The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, 5670047, Ibarakishi, Osaka, Japan |4 aut | |
| 700 | 1 | |a Haffari |D Gholamreza |u Faculty of Information Technology, Monash University, 3168, Clayton, VIC, Australia |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 100/2-3(2015-09-01), 677-699 |x 0885-6125 |q 100:2-3<677 |1 2015 |2 100 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-015-5524-x |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-015-5524-x |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Chen |D Bo |u Faculty of Information Technology, Monash University, 3168, Clayton, VIC, Australia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Ting |D Kai |u School of Engineering and Information Technology, Federation University Australia, 3842, Churchill, VIC, Australia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Washio |D Takashi |u The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, 5670047, Ibarakishi, Osaka, Japan |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Haffari |D Gholamreza |u Faculty of Information Technology, Monash University, 3168, Clayton, VIC, Australia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 100/2-3(2015-09-01), 677-699 |x 0885-6125 |q 100:2-3<677 |1 2015 |2 100 |o 10994 | ||