Feature Learning and Understanding
Algorithms and Applications
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13 Result(s)
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**li Zhang
Physics
Image Processing and Computer Vision
Pattern Recognition
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Book
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Chapter
A Gentle Introduction to Feature Learning
Feature learning is the process of using domain knowledge and special techniques to transform raw data into features. Feature learning can build derived features, eliminate irrelevant, redundant, or noisy data...
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Principal Component Analysis
Principal component analysis (PCA) tries to find an orthogonal linear projection that projects the data into a novel coordinate system, in which the greatest variance by some scalar projection of the data lies...
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Linear Discriminant Analysis
Linear discriminant analysis (LDA) is widely studied in statistics, machine learning, and pattern recognition, which can be considered as a generalization of Fisher’s linear discriminant (FLD). LDA is designed...
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Sparse Feature Learning
The traditional linear feature extraction methods focus ℓ2, 1on data global structure information or data local structure information. Although these learning methods perform well in some real applications to som...
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Tensor-Based Feature Learning
Recently, tensor-based feature learning has attracted great interest in the emergence of multi-linear data. In this chapter, we briefly introduce some Tucker-based feature learning methods. Different from vect...
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Neural-Network-Based Feature Learning: Convolutional Neural Network
Convolutional neural network (CNNs) are a kind of feedforward neural network with convolutional computation and deep structure. In recent years, the application of CNN is very extensive, such as visual images,...
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Latent Semantic Feature Extraction
Latent semantic feature extraction (LSFE) is a feature extraction framework to obtain meaningful features from large volumes of data. In this chapter, we give a brief introduction to LSFE and mainly focus on o...
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Manifold-Learning-Based Feature Extraction
Manifold-learning-based algorithms have widely studied in the last two decades and have been considered as powerful tools for feature learning. The theory of differential geometry shows that the intrinsic geom...
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Kernel-Based Nonlinear Feature Learning
Kernel-based nonlinear feature learning plays an important role in pattern recognition. Before deep learning, the combination of kernels and classical feature learning methods such as principal component analy...
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Low Rank Feature Learning
Low-rank representation (LRR), which constructs a robust low rank representation for data processing, has attracted much attention in the past decades. It is assumed that the data points lie on a low-dimension...
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Neural-Network-Based Feature Learning: Auto-Encoder
In recent years, deep learning has garnered tremendous success in a variety of application domains. As a representative of unsupervised learning in deep learning, auto-encoder (AE) is favored by many researche...
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Neural-Network-Based Feature Learning: Recurrent Neural Network
Recurrent Neural Networks (RNNs) are a class of artificial neural networks for the processing and predicting sequential data, which add recurrent connections feeding the hidden layers of the neural network bac...
