Modified Convex Data Clustering Algorithm Based on Alternating Direction Method of Multipliers

نویسندگان

  • Mohammad Bagher Menhaj Department of Electrical Engineering Amirkabir University of Technology, Tehran, Iran
  • Tahereh Esmaeili Abharian Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
چکیده مقاله:

Knowing the fact that the main weakness of the most standard methods including k-means and hierarchical data clustering is their sensitivity to initialization and trapping to local minima, this paper proposes a modification of convex data clustering  in which there is no need to  be peculiar about how to select initial values. Due to properly converting the task of optimization to an equivalent convex optimization problem, the proposed data clustering algorithm can be indeed considered as a global minimizer. In this paper, a splitting method for solving the convex clustering problem is used called as Alterneting Direction Method of Multipliers (ADMM), a simple but powerful algorithm that is well suited to convex optimization. We demonstrate the performance of the proposed algorithm on real data examples. The simulation result easily approve that the Modified Convex Data Clustering (MCDC) algorithm provides separation more than the Convex Data Clustering (CDC) algorithm. Furthermore, complexity of solving the second part of MCDC problem is reduced from O(n2) to O(n).

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

modified convex data clustering algorithm based on alternating direction method of multipliers

knowing the fact that the main weakness of the most standard methods including k-means and hierarchical data clustering is their sensitivity to initialization and trapping to local minima, this paper proposes a modification of convex data clustering  in which there is no need to  be peculiar about how to select initial values. due to properly converting the task of optimization to an equivalent...

متن کامل

Modified alternating direction method of multipliers for convex quadratic semidefinite programming

The dual form of convex quadratic semidefinite programming (CQSDP) problem, with nonnegative constraints on the matrix variable, is a 4-block convex optimization problem. It is known that, the directly extended 4-block alternating direction method of multipliers (ADMM) is very efficient to solve this dual, but its convergence are not guaranteed. In this paper, we reformulate it as a 3-block con...

متن کامل

Bregman Alternating Direction Method of Multipliers

The mirror descent algorithm (MDA) generalizes gradient descent by using a Bregman divergence to replace squared Euclidean distance. In this paper, we similarly generalize the alternating direction method of multipliers (ADMM) to Bregman ADMM (BADMM), which allows the choice of different Bregman divergences to exploit the structure of problems. BADMM provides a unified framework for ADMM and it...

متن کامل

Adaptive Stochastic Alternating Direction Method of Multipliers

The Alternating Direction Method of Multipliers (ADMM) has been studied for years. Traditional ADMM algorithms need to compute, at each iteration, an (empirical) expected loss function on all training examples, resulting in a computational complexity proportional to the number of training examples. To reduce the complexity, stochastic ADMM algorithms were proposed to replace the expected loss f...

متن کامل

Fast Stochastic Alternating Direction Method of Multipliers

In this paper, we propose a new stochastic alternating direction method of multipliers (ADMM) algorithm, which incrementally approximates the full gradient in the linearized ADMM formulation. Besides having a low per-iteration complexity as existing stochastic ADMM algorithms, the proposed algorithm improves the convergence rate on convex problems from O ( 1 √ T ) to O ( 1 T ) , where T is the ...

متن کامل

An inertial alternating direction method of multipliers

In the context of convex optimization problems in Hilbert spaces, we induce inertial effects into the classical ADMM numerical scheme and obtain in this way so-called inertial ADMM algorithms, the convergence properties of which we investigate into detail. To this aim we make use of the inertial version of the DouglasRachford splitting method for monotone inclusion problems recently introduced ...

متن کامل

منابع من

با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ذخیره در منابع من قبلا به منابع من ذحیره شده

{@ msg_add @}


عنوان ژورنال

دوره 8  شماره 2

صفحات  33- 41

تاریخ انتشار 2015-03-01

با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.

میزبانی شده توسط پلتفرم ابری doprax.com

copyright © 2015-2023