| Description |
1 online resource (1 video file, approximately 18 min.) |
| Summary |
Presented by Namita Lokare Feature engineering plays a significant role in the success of a machine learning model. Most of the effort in training a model goes into data preparation and choosing the right representation. In this talk, I will focus on a robust feature engineering method, Randomized Union of Locally Linear Subspaces (RULLS). We generate sparse, non-negative, and rotation invariant features in an unsupervised fashion. RULLS aggregates features from a random union of subspaces by describing each point using globally chosen landmarks. These landmarks serve as anchor points for choosing subspaces. Our method provides a way to select features that are relevant in the neighborhood around these chosen landmarks. Distances from each data point to k closest landmarks are encoded in the feature matrix. The final feature representation is a union of features from all chosen subspaces. The effectiveness of our algorithm is shown on various real-world datasets for tasks such as clustering and classification of raw data and in the presence of noise. We compare our method with existing feature generation methods. Results show a high performance of our method on both classification and clustering tasks |
| Notes |
Mode of access: World Wide Web |
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Copyright © Formulatedby 2019 |
| Issuing Body |
Made available through: Safari, an O'Reilly Media Company |
| Notes |
Online resource; Title from title screen (viewed September 10, 2019) |
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Internet videos.
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Streaming video.
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streaming video.
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| Form |
Streaming video
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| Author |
Safari, an O'Reilly Media Company
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