Graph Embedding for Pattern Analysis

¡
¡ Springer Science & Business Media
āχ-āĻŦ⧁āĻ•
260
āĻĒ⧃āĻˇā§āĻ āĻž
āϰ⧇āϟāĻŋāĻ‚ āĻ“ āϰāĻŋāĻ­āĻŋāω āϝāĻžāϚāĻžāχ āĻ•āϰāĻž āĻšā§ŸāύāĻŋ  āφāϰāĻ“ āϜāĻžāύ⧁āύ

āĻāχ āχ-āĻŦ⧁āϕ⧇āϰ āĻŦāĻŋāĻˇā§Ÿā§‡

Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.

āϞ⧇āĻ–āĻ• āϏāĻŽā§āĻĒāĻ°ā§āϕ⧇

Dr. Yun Fu is a professor at the State University of New York at Buffalo
Dr. Yunqian Ma is a senior principal research scientist of Honeywell Labs at the Honeywell International Inc.

āχ-āĻŦ⧁āϕ⧇ āϰ⧇āϟāĻŋāĻ‚ āĻĻāĻŋāύ

āφāĻĒāύāĻžāϰ āĻŽāϤāĻžāĻŽāϤ āϜāĻžāύāĻžāύāĨ¤

āĻĒāĻ āύ āϤāĻĨā§āϝ

āĻ¸ā§āĻŽāĻžāĻ°ā§āϟāĻĢā§‹āύ āĻāĻŦāĻ‚ āĻŸā§āϝāĻžāĻŦāϞ⧇āϟ
Android āĻāĻŦāĻ‚ iPad/iPhone āĻāϰ āϜāĻ¨ā§āϝ Google Play āĻŦāχ āĻ…ā§āϝāĻžāĻĒ āχāύāĻ¸ā§āϟāϞ āĻ•āϰ⧁āύāĨ¤ āĻāϟāĻŋ āφāĻĒāύāĻžāϰ āĻ…ā§āϝāĻžāĻ•āĻžāωāĻ¨ā§āĻŸā§‡āϰ āϏāĻžāĻĨ⧇ āĻ…āĻŸā§‹āĻŽā§‡āϟāĻŋāĻ• āϏāĻŋāĻ™ā§āĻ• āĻšā§Ÿ āĻ“ āφāĻĒāύāĻŋ āĻ…āύāϞāĻžāχāύ āĻŦāĻž āĻ…āĻĢāϞāĻžāχāύ āϝāĻžāχ āĻĨāĻžāϕ⧁āύ āύāĻž āϕ⧇āύ āφāĻĒāύāĻžāϕ⧇ āĻĒ⧜āϤ⧇ āĻĻā§‡ā§ŸāĨ¤
āĻ˛ā§āϝāĻžāĻĒāϟāĻĒ āĻ“ āĻ•āĻŽā§āĻĒāĻŋāωāϟāĻžāϰ
Google Play āĻĨ⧇āϕ⧇ āϕ⧇āύāĻž āĻ…āĻĄāĻŋāĻ“āĻŦ⧁āĻ• āφāĻĒāύāĻŋ āĻ•āĻŽā§āĻĒāĻŋāωāϟāĻžāϰ⧇āϰ āĻ“ā§Ÿā§‡āĻŦ āĻŦā§āϰāĻžāωāϜāĻžāϰ⧇ āĻļ⧁āύāϤ⧇ āĻĒāĻžāϰ⧇āύāĨ¤
eReader āĻāĻŦāĻ‚ āĻ…āĻ¨ā§āϝāĻžāĻ¨ā§āϝ āĻĄāĻŋāĻ­āĻžāχāϏ
Kobo eReaders-āĻāϰ āĻŽāϤ⧋ e-ink āĻĄāĻŋāĻ­āĻžāχāϏ⧇ āĻĒāĻĄāĻŧāϤ⧇, āφāĻĒāύāĻžāϕ⧇ āĻāĻ•āϟāĻŋ āĻĢāĻžāχāϞ āĻĄāĻžāωāύāϞ⧋āĻĄ āĻ“ āφāĻĒāύāĻžāϰ āĻĄāĻŋāĻ­āĻžāχāϏ⧇ āĻŸā§āϰāĻžāĻ¨ā§āϏāĻĢāĻžāϰ āĻ•āϰāϤ⧇ āĻšāĻŦ⧇āĨ¤ āĻŦā§āϝāĻŦāĻšāĻžāϰāĻ•āĻžāϰ⧀āϰ āωāĻĻā§āĻĻ⧇āĻļā§āϝ⧇ āϤ⧈āϰāĻŋ āϏāĻšāĻžā§ŸāϤāĻž āϕ⧇āĻ¨ā§āĻĻā§āϰāϤ⧇ āĻĻ⧇āĻ“ā§ŸāĻž āύāĻŋāĻ°ā§āĻĻ⧇āĻļāĻžāĻŦāϞ⧀ āĻ…āύ⧁āϏāϰāĻŖ āĻ•āϰ⧇ āϝ⧇āϏāĻŦ eReader-āĻ āĻĢāĻžāχāϞ āĻĒāĻĄāĻŧāĻž āϝāĻžāĻŦ⧇ āϏ⧇āĻ–āĻžāύ⧇ āĻŸā§āϰāĻžāĻ¨ā§āϏāĻĢāĻžāϰ āĻ•āϰ⧁āύāĨ¤