Orthogonal Neighborhood Preserving Projections: A Projection-Based Dimensionality Reduction Technique
نویسندگان
چکیده
منابع مشابه
Neighborhood Preserving Projections (NPP): A Novel Linear Dimension Reduction Method
Dimension reduction is a crucial step for pattern recognition and information retrieval tasks to overcome the curse of dimensionality. In this paper a novel unsupervised linear dimension reduction method, Neighborhood Preserving Projections (NPP), is proposed. In contrast to traditional linear dimension reduction method, such as principal component analysis (PCA), the proposed method has good n...
متن کاملDiscriminant Uncorrelated Neighborhood Preserving Projections ?
Dimensionality reduction is a crucial step for pattern recognition. Recently, a new kind of dimensionality reduction method, manifold learning, has attracted much attention.Among them, Neighborhood Preserving Projections (NPP) is one of the most promising techniques. In this paper, a novel manifold learning method called Discriminant Uncorrelated Neighborhood Preserving Projections (DUNPP), is ...
متن کاملInformation Preserving Dimensionality Reduction
Dimensionality reduction is a very common preprocessing approach in many machine learning tasks. The goal is to design data representations that on one hand reduce the dimension of the data (therefore allowing faster processing), and on the other hand aim to retain as much task-relevant information as possible. We look at generic dimensionality reduction approaches that do not rely on much task...
متن کاملLabel Preserving Dimensionality Reduction
Many tasks, such as face recognition, require learning a classifier from a small number of high dimensional training samples. These tasks suffer from the curse of dimensionality: the number of training samples required to accurately learn a classifier increases exponentially with the dimensionality of the data. One solution to this problem is dimensionality reduction. Common methods for dimensi...
متن کاملFlexible Orthogonal Neighborhood Preserving Embedding
In this paper, we propose a novel linear subspace learning algorithm called Flexible Orthogonal Neighborhood Preserving Embedding (FONPE), which is a linear approximation of Locally Linear Embedding (LLE) algorithm. Our novel objective function integrates two terms related to manifold smoothness and a flexible penalty defined on the projection fitness. Different from Neighborhood Preserving Emb...
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 Transactions on Pattern Analysis and Machine Intelligence
سال: 2007
ISSN: 0162-8828
DOI: 10.1109/tpami.2007.1131