A Nonlinear Dimensionality Reduction Using Combined Approach to Feature Space Decomposition

نویسنده

  • Evgeny Myasnikov
چکیده

In this paper we propose a new combined approach to feature space decomposition to improve the efficiency of the nonlinear dimensionality reduction method. The approach performs the decomposition of the original multidimensional space, taking into account the configuration of objects in the target low-dimensional space. The proposed approach is compared to the approach using hierarchical clustering in the original space and to the approach based on the decomposition of the target space using KD-Tree.

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

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

مدل ترکیبی تحلیل مؤلفه اصلی احتمالاتی بانظارت در چارچوب کاهش بعد بدون اتلاف برای شناسایی چهره

In this paper, we first proposed the supervised version of probabilistic principal component analysis mixture model. Then, we consider a learning predictive model with projection penalties, as an approach for dimensionality reduction without loss of information for face recognition. In the proposed method, first a local linear underlying manifold of data samples is obtained using the supervised...

متن کامل

Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical lo...

متن کامل

Model Reduction for Nonlinear Control Systems using Kernel Subspace Methods

We introduce a data-driven model approximation method for nonlinear control systems, drawing on recent progress in machine learning and statistical dimensionality reduction. The method is based on embedding the nonlinear system in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where linear balanced truncation may be carried out implicitly. This leads to a nonlinear red...

متن کامل

Steel Consumption Forecasting Using Nonlinear Pattern Recognition Model Based on Self-Organizing Maps

Steel consumption is a critical factor affecting pricing decisions and a key element to achieve sustainable industrial development. Forecasting future trends of steel consumption based on analysis of nonlinear patterns using artificial intelligence (AI) techniques is the main purpose of this paper. Because there are several features affecting target variable which make the analysis of relations...

متن کامل

Multi-Label Classification Using Dependent and Independent Dual Space Reduction

While multi-label classification can be widely applied for problems where multiple classes can be assigned to an object, its effectiveness may be sacrificed due to curse of dimensionality in the feature space and sparseness of dimensionality in the label space. As a solution, this paper presents two alternative methods, namely Dependent Dual Space Reduction and Independent Dual Space Reduction,...

متن کامل

ذخیره در منابع من


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

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

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2015