2024年11月15日 · LPP ranked first in this year’s 6th edition of the Corporate Climate Crisis Awareness Study (CCA). Among the 163 companies assessed in the study, LPP scored the maximum number of points, excelling in transparency and clearly defined CO2 reduction targets.

2012年4月18日 · This paper proposes a unified least-squares framework to formulate many CA methods. We show how PCA, LDA, CCA, LPP, SC, and its kernel and regularized extensions correspond to a particular instance of least-squares weighted kernel reduced rank regression (LS- …

lpp就是一个典型的降维算法，它的通过构建空间中各样本对之间的远近亲疏关系，并在降维投影中尽可能地去保留这样的亲疏关系，从而保留数据的局部结构。

2007年5月1日 · Inspired by the locality based methods, in this paper, we incorporate such an idea into CCA and propose locality preserving CCA (LPCCA) to discover the local manifold structure of the data and further apply it to data visualization and pose estimation.

2022年7月14日 · LPCCA can learn two sets of projections containing local features by embedding a similar Laplacian graph into the CCA framework. Adopting the regularization technique, heterogeneous structure fusion (HSF) combines LPP and CCA to extract the feature information in multiple raw spaces. Moreover, some variant methods of CCA with discriminate power ...

2016年3月5日 · 该方法通过结合主成分分析(pca)与局部保持嵌入(lpp)各自优点，兼具最大化保留数据的全局结构特性与局部结构特性，更利于提取原始数据集中的低维流行有效信息。

2018年4月5日 · In contrast, in this paper we propose a novel two-view method named as two-dimensional locality preserving canonical correlation analysis (2D-LPCCA), which uses the neighborhood information to discover the intrinsic structure of data. In other words, it uses many local linear problems to approximate the global nonlinear case.

With regard to the high dimensional and small sample facial feature, this paper introduced the Kernel and Canonical Correlation Analysis (CCA) into the Locality Preserving Projections (LPP) algorithm and proposed a new face recognition algorithm based on the Kernel Base Locality Preserving Canonical Correlation Analysis (KLPCCA) with the ...

2022年2月12日 · 局部保持投影（Locality Preserving Projections，LPP）是一种非线性降维技术，它在数据挖掘、机器学习和模式识别领域具有广泛的应用。LPP 的主要目标是保留数据集中的局部结构，在低维空间中保持高维数据点之间的...

2020年12月24日 · CCA(Canonical Correlation Analysis)： 无监督学习，对两组变量降维，找到一个最优相关子空间进行相关性分析。 应用：问卷调查中变量的相关性分析、跨模态学习。 LDA： 有监督学习，学习一个可分性最好的投影方向。相当于是白化(whitening) + PCA，得到的是假设条 …