Unsupervised Feature Learning Via Sparse Hierarchical Representations
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Unsupervised Feature Learning Via Sparse Hierarchical Representations
Machine learning has proved a powerful tool for artificial intelligence and data mining problems. However, its success has usually relied on having a good feature representation of the data, and having a poor representation can severely limit the performance of learning algorithms. These feature representations are often hand-designed, require significant amounts of domain knowledge and human labor, and do not generalize well to new domains. To address these issues, I will present machine learning algorithms that can automatically learn good feature representations from unlabeled data in various domains, such as images, audio, text, and robotic sensors. Specifically, I will first describe how efficient sparse coding algorithms --- which represent each input example using a small number of basis vectors --- can be used to learn good low-level representations from unlabeled data. I also show that this gives feature representations that yield improved performance in many machine learning tasks. In addition, building on the deep learning framework, I will present two new algorithms, sparse deep belief networks and convolutional deep belief networks, for building more complex, hierarchical representations, in which more complex features are automatically learned as a composition of simpler ones. When applied to images, this method automatically learns features that correspond to objects and decompositions of objects into object-parts. These features often lead to performance competitive with or better than highly hand-engineered computer vision algorithms in object recognition and segmentation tasks. Further, the same algorithm can be used to learn feature representations from audio data. In particular, the learned features yield improved performance over state-of-the-art methods in several speech recognition tasks.
Unsupervised Feature Learning Via Sparse Hierarchical Representations
Machine learning has proved a powerful tool for artificial intelligence and data mining problems. However, its success has usually relied on having a good feature representation of the data, and having a poor representation can severely limit the performance of learning algorithms. These feature representations are often hand-designed, require significant amounts of domain knowledge and human labor, and do not generalize well to new domains. To address these issues, I will present machine learning algorithms that can automatically learn good feature representations from unlabeled data in various domains, such as images, audio, text, and robotic sensors. Specifically, I will first describe how efficient sparse coding algorithms --- which represent each input example using a small number of basis vectors --- can be used to learn good low-level representations from unlabeled data. I also show that this gives feature representations that yield improved performance in many machine learning tasks. In addition, building on the deep learning framework, I will present two new algorithms, sparse deep belief networks and convolutional deep belief networks, for building more complex, hierarchical representations, in which more complex features are automatically learned as a composition of simpler ones. When applied to images, this method automatically learns features that correspond to objects and decompositions of objects into object-parts. These features often lead to performance competitive with or better than highly hand-engineered computer vision algorithms in object recognition and segmentation tasks. Further, the same algorithm can be used to learn feature representations from audio data. In particular, the learned features yield improved performance over state-of-the-art methods in several speech recognition tasks.
DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION
Author: Srinivas Babu Ratnam
language: en
Publisher: Xoffencerpublication
Release Date: 2023-07-03
Several empirical research have come to the conclusion that the representation of data plays a vital role in the efficiency with which machine learning algorithms complete their tasks. This indicates that the design of feature extraction, preprocessing, and data transformations requires a disproportionate amount of time and resources when actually executing machine learning algorithms. These steps include preparing the data for analysis, extracting features from the data, and processing the data. This is because each of these components is essential to the algorithm as a whole in order for it to function properly. In spite of the fact that it is of the utmost significance, feature engineering calls for a significant amount of human effort. It also shows a shortcoming of the learning algorithms that are now in use, which is their inability to extract all of the pertinent characteristics from the data that is currently accessible. This is a difficulty with the approaches that are currently utilized in the process of learning. An approach that may be utilized to make up for such a shortfall is called feature engineering, and it involves making use of human intelligence in conjunction with prior information. It would be extremely desired to make learning algorithms less dependent on feature engineering in order to expedite the production of innovative applications and, more crucially, to realize advancements in artificial intelligence (AI). This would be done in order to achieve developments in AI. There are two possible consequences resulting from this. This would make it possible to use machine learning in a larger variety of applications that are simpler to put into action, which would increase the value of machine learning. An artificial intelligence has to have at least a fundamental comprehension of the environment in which humans live, and this may be accomplished if a learner is able to interpret the concealed explanatory factors that are embedded within the visible milieu of low-level sensory input. It is conceivable to combine feature engineering with feature learning in order to obtain state-of-the-art solutions that can be applied to actual circumstances in the real world.