A comment on "Laplacian linear discriminant analysis"

نویسنده

  • Zilan Hu
چکیده

The recent paper by Tang et al. [1] has proven that Fisher’s criterion function is equal to a novel linear discriminant criterion function. The novel function is then related to spectral decomposition of the Laplacian of a graph. The equivalence between the two functions is based on Theorem 1 given in the beginning of Ref. [1]. Although some mathematical analysis is established in their paper, we will show in this comment that Theorem 1 that is fundamental for the paper [1] is not true in general case. Theorem 1 given in Ref. [1] is stated as follows.

برای دانلود رایگان 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

Quantum Laplacian Eigenmap

Laplacian eigenmap algorithm is a typical nonlinear model for dimensionality reduction in classical machine learning. We propose an efficient quantum Laplacian eigenmap algorithm to exponentially speed up the original counterparts. In our work, we demonstrate that the Hermitian chain product proposed in quantum linear discriminant analysis (arXiv:1510.00113,2015) can be applied to implement qua...

متن کامل

Experiments with linear feature extraction in speech recognition

In this paper we investigate Linear Discriminant Analysis (LDA) for the TI connected digit recognition task (TI task) and the Wall Street Journal large vocabulary recognition task (WSJ task). In addition to previous variants of LDA implementations, we avoided the explicit incorporation of derivatives in the acoustic vector. Instead a sliding window without derivatives was used. This large-sized...

متن کامل

The University of Chicago Locality Preserving Projections a Dissertation Submitted to the Faculty of the Division of the Physical Sciences in Candidacy for the Degree of Doctor of Philosophy Department of Computer Science By

Many problems in information processing involve some form of dimensionality reduction. In this thesis, we introduce Locality Preserving Projections (LPP). These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the data set. LPP should be seen as an alternative to Principal Component Analysis (PCA) – a classical linear ...

متن کامل

Laplacian MinMax Discriminant Projection and its Applications

A new algorithm, Laplacian MinMax Discriminant Projection (LMMDP), is proposed in this paper for supervised dimensionality reduction. LMMDP aims at learning a discriminant linear transformation. Specifically, we define the within-class scatter and the between-class scatter using similarities which are based on pairwise distances in sample space. After the transformation, the considered pairwise...

متن کامل

Differential-Private Data Publishing Through Component Analysis

A reasonable compromise of privacy and utility exists at an "appropriate" resolution of the data. We proposed novel mechanisms to achieve privacy preserving data publishing (PPDP) satisfying ε-differential privacy with improved utility through component analysis. The mechanisms studied in this article are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The differentia...

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

عنوان ژورنال:
  • Pattern Recognition

دوره 41  شماره 

صفحات  -

تاریخ انتشار 2008