This book is a quick guide that will help you through the basics steps to get you started selling products online. Using a drop shipping distributor you will be able to sell online without ever having to touch the merchandise. Your entire store can be running from your bedroom, black socks are optional. The customer orders a product from you, then you place the order with your distributor. The drop ship distributor will then ship the merchandise directly to your customer. If you have made online purchases there is a chance that you might have bought from a drop shipper. The book was written as a quick guide to give you the tools that you need to get started. After closing my Real Estate Company, I was searching for any easy, no stress business to start. I didn't want huge overhead, and having to work 80 hour weeks. I already had a full time job, and wanted to supplement my income without losing time from my family. Selling online had its appeal. I read several books on the topic. Some were very well written, and others filled my head on how I could be a millionaire. Although some of the books were great motivators, they didn't make me a millionaire. I wrote this book to help you start your first online store with realistic outlooks. Once the store is set up, it will not demand much of your time, or take you away from your family. Since the startup cost is low, you can take the skills that you learned from opening your first online store, and open up several more. Opening an online reseller store will not make you instantly rich, but it will be a nice supplement to your income.
Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and classical metric multidimensional scaling suffer from being based on linear models. Until recently, very few methods were able to reduce the data dimensionality in a nonlinear way. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. New advances that account for this rapid growth are, e.g. the use of graphs to represent the manifold topology, and the use of new metrics like the geodesic distance. In addition, new optimization schemes, based on kernel techniques and spectral decomposition, have lead to spectral embedding, which encompasses many of the recently developed methods.
This book describes existing and advanced methods to reduce the dimensionality of numerical databases. For each method, the description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. Methods are compared with each other with the help of different illustrative examples.
The purpose of the book is to summarize clear facts and ideas about well-known methods as well as recent developments in the topic of nonlinear dimensionality reduction. With this goal in mind, methods are all described from a unifying point of view, in order to highlight their respective strengths and shortcomings.
The book is primarily intended for statisticians, computer scientists and data analysts. It is also accessible to other practitioners having a basic background in statistics and/or computational learning, like psychologists (in psychometry) and economists.
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