An Adaptive Density Peaks Clustering Method With Fisher Linear Discriminant
نویسندگان
چکیده
منابع مشابه
An Adaptive Method for Clustering by Fast Search-and-Find of Density Peaks: Adaptive-DP
Clustering by fast search and find of density peaks (DP) is a method in which density peaks are used to select the number of cluster centers. The DP has two input parameters: 1) the cutoff distance and 2) cluster centers. Also in DP, different methods are used to measure the density of underlying datasets. To overcome the limitations of DP, an Adaptive-DP method is proposed. In Adaptive-DP meth...
متن کاملFisher Linear Discriminant Analysis
Fisher Linear Discriminant Analysis (also called Linear Discriminant Analysis(LDA)) are methods used in statistics, pattern recognition and machine learning to find a linear combination of features which characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later c...
متن کاملDensity Peaks Clustering with Differential Privacy
Density peaks clustering (DPC) is a latest and well-known density-based clustering algorithm which offers advantages for finding clusters of arbitrary shapes compared to others algorithm. However, the attacker can deduce sensitive points from the known point when the cluster centers and sizes are exactly released in the cluster analysis. To the best of our knowledge, this is the first time that...
متن کاملSparsifying the Fisher Linear Discriminant by Rotation.
Many high dimensional classification techniques have been proposed in the literature based on sparse linear discriminant analysis (LDA). To efficiently use them, sparsity of linear classifiers is a prerequisite. However, this might not be readily available in many applications, and rotations of data are required to create the needed sparsity. In this paper, we propose a family of rotations to c...
متن کاملAn On-line Fisher Discriminant
Many applications in signal processing need an adaptive algorithm. Adaptive schemes are useful when the statistics of the problem are unknown or when facing varying environments. Nonetheless, many of these applications deal with classification tasks, and most algorithms are not specifically thought to tackle these kinds of problems. Whereas Fisher’s criterion aimed to find the most adequate dir...
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: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2918952